Date: (Fri) Feb 05, 2016

Introduction:

Data: Source: Training: https://www.kaggle.com/c/yelp-restaurant-photo-classification/download/train.csv.tgz
New: https://www.kaggle.com/c/yelp-restaurant-photo-classification/download/test.csv.tgz
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<pointer>"; if url specifies a zip file, name = "<filename>"
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://www.kaggle.com/c/yelp-restaurant-photo-classification/download/train.csv.tgz",
                      name = "train_resXY.csv") 

glbObsNewFile <- list(url = "https://www.kaggle.com/c/yelp-restaurant-photo-classification/download/test.csv.tgz",
                      name = "test_resXY.csv") # default OR
    #list(splitSpecs = list(method = NULL #select from c(NULL, "condition", "sample", "copy")
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    #    )                   

glbInpMerge <- NULL #: default
#     list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated

glb_is_separate_newobs_dataset <- TRUE    # or TRUE
    glb_split_entity_newobs_datasets <- TRUE  # FALSE not supported - use "copy" for glbObsNewFile$splitSpecs$method # select from c(FALSE, TRUE)

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "outdoor"

# for classification, the response variable has to be a factor
glb_rsp_var <- "outdoor.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] != -1, "Y", "N"); return(relevel(as.factor(ret_vals), ref = "N"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))    
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
#print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany")) 

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
    levels(var)[as.numeric(var)]
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
#print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "business_id" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "nImgs.cut.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
    ,"labels"
    ,"lunch","dinner","reserve","outdoor","expensive","liquor","table","classy","kids"
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & work each one in
    ,"business_id"
    ,"resXLst","resYLst"
                    ) 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

glb_assign_pairs_lst <- NULL; 
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
#                                            to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(.rnorm) { return(1:length(.rnorm)) }       
    , args = c(".rnorm"))    
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(.rnorm) { return(1:length(.rnorm)) }       
#     , args = c(".rnorm"))    

glbFeatsDerive[["nImgs.log1p"]] <- list(
    mapfn = function(nImgs) { return(log1p(nImgs)) } 
  , args = c("nImgs"))
glbFeatsDerive[["nImgs.root2"]] <- list(
    mapfn = function(nImgs) { return(nImgs ^ (1/2)) } 
  , args = c("nImgs"))
glbFeatsDerive[["nImgs.nexp"]] <- list(
    mapfn = function(nImgs) { return(exp(-nImgs)) } 
  , args = c("nImgs"))

glbFeatsDerive[["resX.min"]] <- list(
    mapfn = function(resXLst) { return(sapply(resXLst, function(thsObsFeat)
                                        min(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resXLst"))
glbFeatsDerive[["resX.max"]] <- list(
    mapfn = function(resXLst) { return(sapply(resXLst, function(thsObsFeat)
                                        max(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resXLst"))
glbFeatsDerive[["resX.mean"]] <- list(
    mapfn = function(resXLst) { return(sapply(resXLst, function(thsObsFeat)
                                        mean(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resXLst"))
glbFeatsDerive[["resX.mad"]] <- list(
    mapfn = function(resXLst) { return(sapply(resXLst, function(thsObsFeat)
                                        mad(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resXLst"))

glbFeatsDerive[["resX.min.log1p"]] <- list(
    mapfn = function(resX.min) { return(log1p(resX.min)) } 
  , args = c("resX.min"))
glbFeatsDerive[["resX.min.root2"]] <- list(
    mapfn = function(resX.min) { return(resX.min ^ (1/2)) } 
  , args = c("resX.min"))
glbFeatsDerive[["resX.min.nexp"]] <- list(
    mapfn = function(resX.min) { return(exp(-resX.min)) } 
  , args = c("resX.min"))

glbFeatsDerive[["resX.max.log1p"]] <- list(
    mapfn = function(resX.max) { return(log1p(resX.max)) } 
  , args = c("resX.max"))
glbFeatsDerive[["resX.max.root2"]] <- list(
    mapfn = function(resX.max) { return(resX.max ^ (1/2)) } 
  , args = c("resX.max"))
glbFeatsDerive[["resX.max.nexp"]] <- list(
    mapfn = function(resX.max) { return(exp(-resX.max)) } 
  , args = c("resX.max"))

glbFeatsDerive[["resX.mean.log1p"]] <- list(
    mapfn = function(resX.mean) { return(log1p(resX.mean)) } 
  , args = c("resX.mean"))
glbFeatsDerive[["resX.mean.root2"]] <- list(
    mapfn = function(resX.mean) { return(resX.mean ^ (1/2)) } 
  , args = c("resX.mean"))
glbFeatsDerive[["resX.mean.nexp"]] <- list(
    mapfn = function(resX.mean) { return(exp(-resX.mean)) } 
  , args = c("resX.mean"))

glbFeatsDerive[["resX.mad.log1p"]] <- list(
    mapfn = function(resX.mad) { return(log1p(resX.mad)) } 
  , args = c("resX.mad"))
glbFeatsDerive[["resX.mad.root2"]] <- list(
    mapfn = function(resX.mad) { return(resX.mad ^ (1/2)) } 
  , args = c("resX.mad"))
glbFeatsDerive[["resX.mad.nexp"]] <- list(
    mapfn = function(resX.mad) { return(exp(-resX.mad)) } 
  , args = c("resX.mad"))

glbFeatsDerive[["resY.min"]] <- list(
    mapfn = function(resYLst) { return(sapply(resYLst, function(thsObsFeat)
                                        min(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resYLst"))
glbFeatsDerive[["resY.max"]] <- list(
    mapfn = function(resYLst) { return(sapply(resYLst, function(thsObsFeat)
                                        max(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resYLst"))
glbFeatsDerive[["resY.mean"]] <- list(
    mapfn = function(resYLst) { return(sapply(resYLst, function(thsObsFeat)
                                        mean(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resYLst"))
glbFeatsDerive[["resY.mad"]] <- list(
    mapfn = function(resYLst) { return(sapply(resYLst, function(thsObsFeat)
                                        mad(as.numeric(unlist(str_split(thsObsFeat, ",")))))) }
  , args = c("resYLst"))

glbFeatsDerive[["resY.min.log1p"]] <- list(
    mapfn = function(resY.min) { return(log1p(resY.min)) } 
  , args = c("resY.min"))
glbFeatsDerive[["resY.min.root2"]] <- list(
    mapfn = function(resY.min) { return(resY.min ^ (1/2)) } 
  , args = c("resY.min"))
glbFeatsDerive[["resY.min.nexp"]] <- list(
    mapfn = function(resY.min) { return(exp(-resY.min)) } 
  , args = c("resY.min"))

glbFeatsDerive[["resY.max.log1p"]] <- list(
    mapfn = function(resY.max) { return(log1p(resY.max)) } 
  , args = c("resY.max"))
glbFeatsDerive[["resY.max.root2"]] <- list(
    mapfn = function(resY.max) { return(resY.max ^ (1/2)) } 
  , args = c("resY.max"))
glbFeatsDerive[["resY.max.nexp"]] <- list(
    mapfn = function(resY.max) { return(exp(-resY.max)) } 
  , args = c("resY.max"))

glbFeatsDerive[["resY.mean.log1p"]] <- list(
    mapfn = function(resY.mean) { return(log1p(resY.mean)) } 
  , args = c("resY.mean"))
glbFeatsDerive[["resY.mean.root2"]] <- list(
    mapfn = function(resY.mean) { return(resY.mean ^ (1/2)) } 
  , args = c("resY.mean"))
glbFeatsDerive[["resY.mean.nexp"]] <- list(
    mapfn = function(resY.mean) { return(exp(-resY.mean)) } 
  , args = c("resY.mean"))

glbFeatsDerive[["resY.mad.log1p"]] <- list(
    mapfn = function(resY.mad) { return(log1p(resY.mad)) } 
  , args = c("resY.mad"))
glbFeatsDerive[["resY.mad.root2"]] <- list(
    mapfn = function(resY.mad) { return(resY.mad ^ (1/2)) } 
  , args = c("resY.mad"))
glbFeatsDerive[["resY.mad.nexp"]] <- list(
    mapfn = function(resY.mad) { return(exp(-resY.mad)) } 
  , args = c("resY.mad"))

glbFeatsDerive[["resXY.min"]] <- list(
    mapfn = function(resXLst, resYLst) {
        resXYAll <- c()
        for (obsIx in 1:length(resXLst)) {
            resX <- as.numeric(unlist(str_split(resXLst[obsIx], ",")))            
            resY <- as.numeric(unlist(str_split(resYLst[obsIx], ",")))
            resXYAll <- c(resXYAll, min(resX * resY))
        }
        return(resXYAll)
    }
  , args = c("resXLst","resYLst"))
glbFeatsDerive[["resXY.max"]] <- list(
    mapfn = function(resXLst, resYLst) {
        resXYAll <- c()
        for (obsIx in 1:length(resXLst)) {
            resX <- as.numeric(unlist(str_split(resXLst[obsIx], ",")))            
            resY <- as.numeric(unlist(str_split(resYLst[obsIx], ",")))
            resXYAll <- c(resXYAll, max(resX * resY))
        }
        return(resXYAll)
    }
  , args = c("resXLst","resYLst"))
glbFeatsDerive[["resXY.mean"]] <- list(
    mapfn = function(resXLst, resYLst) {
        resXYAll <- c()
        for (obsIx in 1:length(resXLst)) {
            resX <- as.numeric(unlist(str_split(resXLst[obsIx], ",")))            
            resY <- as.numeric(unlist(str_split(resYLst[obsIx], ",")))
            resXYAll <- c(resXYAll, mean(resX * resY))
        }
        return(resXYAll)
    }
  , args = c("resXLst","resYLst"))
glbFeatsDerive[["resXY.mad"]] <- list(
    mapfn = function(resXLst, resYLst) {
        resXYAll <- c()
        for (obsIx in 1:length(resXLst)) {
            resX <- as.numeric(unlist(str_split(resXLst[obsIx], ",")))            
            resY <- as.numeric(unlist(str_split(resYLst[obsIx], ",")))
            resXYAll <- c(resXYAll, mad(resX * resY))
        }
        return(resXYAll)
    }
  , args = c("resXLst","resYLst"))

glbFeatsDerive[["resXY.min.log1p"]] <- list(
    mapfn = function(resXY.min) { return(log1p(resXY.min)) } 
  , args = c("resXY.min"))
glbFeatsDerive[["resXY.min.root2"]] <- list(
    mapfn = function(resXY.min) { return(resXY.min ^ (1/2)) } 
  , args = c("resXY.min"))
glbFeatsDerive[["resXY.min.nexp"]] <- list(
    mapfn = function(resXY.min) { return(exp(-resXY.min)) } 
  , args = c("resXY.min"))

glbFeatsDerive[["resXY.max.log1p"]] <- list(
    mapfn = function(resXY.max) { return(log1p(resXY.max)) } 
  , args = c("resXY.max"))
glbFeatsDerive[["resXY.max.root2"]] <- list(
    mapfn = function(resXY.max) { return(resXY.max ^ (1/2)) } 
  , args = c("resXY.max"))
glbFeatsDerive[["resXY.max.nexp"]] <- list(
    mapfn = function(resXY.max) { return(exp(-resXY.max)) } 
  , args = c("resXY.max"))

glbFeatsDerive[["resXY.mean.log1p"]] <- list(
    mapfn = function(resXY.mean) { return(log1p(resXY.mean)) } 
  , args = c("resXY.mean"))
glbFeatsDerive[["resXY.mean.root2"]] <- list(
    mapfn = function(resXY.mean) { return(resXY.mean ^ (1/2)) } 
  , args = c("resXY.mean"))
glbFeatsDerive[["resXY.mean.nexp"]] <- list(
    mapfn = function(resXY.mean) { return(exp(-resXY.mean)) } 
  , args = c("resXY.mean"))

glbFeatsDerive[["resXY.mad.log1p"]] <- list(
    mapfn = function(resXY.mad) { return(log1p(resXY.mad)) } 
  , args = c("resXY.mad"))
glbFeatsDerive[["resXY.mad.root2"]] <- list(
    mapfn = function(resXY.mad) { return(resXY.mad ^ (1/2)) } 
  , args = c("resXY.mad"))
glbFeatsDerive[["resXY.mad.nexp"]] <- list(
    mapfn = function(resXY.mad) { return(exp(-resXY.mad)) } 
  , args = c("resXY.mad"))

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor
glbFeatsDerive[["lunch"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(0)), "0", "-1") })
        , levels = c("-1", "0"))) }       
    , args = c("labels"))    
glbFeatsDerive[["dinner"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(1)), "1", "-1") })
        , levels = c("-1", "1"))) }       
    , args = c("labels"))    
glbFeatsDerive[["reserve"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(2)), "2", "-1") })
        , levels = c("-1", "2"))) }       
    , args = c("labels"))    
glbFeatsDerive[["outdoor"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(3)), "3", "-1") })
        , levels = c("-1", "3"))) }       
    , args = c("labels"))    
glbFeatsDerive[["expensive"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(4)), "4", "-1") })
        , levels = c("-1", "4"))) }       
    , args = c("labels"))    
glbFeatsDerive[["liquor"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(5)), "5", "-1") })
        , levels = c("-1", "5"))) }       
    , args = c("labels"))    
glbFeatsDerive[["table"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(6)), "6", "-1") })
        , levels = c("-1", "6"))) }       
    , args = c("labels"))    
glbFeatsDerive[["classy"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(7)), "7", "-1") })
        , levels = c("-1", "7"))) }       
    , args = c("labels"))    
glbFeatsDerive[["kids"]] <- list(
    mapfn = function(labels) { return(factor(
        sapply(labels, function(obsLabel) {if (is.na(obsLabel)) return(NA); 
            ifelse(any(as.numeric(unlist(str_split(obsLabel, " "))) %in% c(8)), "8", "-1") })
        , levels = c("-1", "8"))) }       
    , args = c("labels"))    
glbFeatsDerive[["nImgs.cut.fctr"]] <- list(
    mapfn = function(nImgs) { return(cut(nImgs, breaks = c(0, 32, 60, 120, 3000))) } 
    , args = c("nImgs"))    

#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

glb_derive_vars <- names(glbFeatsDerive)

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE, 
#       last.ctg = TRUE, poly.ctg = TRUE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")  # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsout_df) {
#     require(tidyr)
#     obsout_df <- obsout_df %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#     
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsout_df) {
#                   }
                  )
#obsout_df <- savobsout_df
glbObsOut$mapFn <- function(obsout_df) {
    set.seed(997)
    txfout_df <- obsout_df %>%
        dplyr::mutate(
            lunch     = levels(glbObsTrn[, "lunch"    ])[
                       round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
            dinner    = levels(glbObsTrn[, "dinner"   ])[
                       round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
            reserve   = levels(glbObsTrn[, "reserve"  ])[
                       round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                 rbinom(nrow(obsout_df), 1, mean(as.numeric(glbObsTrn[, "outdoor"  ])) - 1) + 1],
            outdoor   = 
        ifelse(levels(glbObsTrn[, "outdoor.fctr"  ])[as.numeric(outdoor.fctr)] == "N", "-1", "3"),
            expensive = levels(glbObsTrn[, "expensive"])[
                       round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
            liquor    = levels(glbObsTrn[, "liquor"   ])[
                       round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
            table     = levels(glbObsTrn[, "table"    ])[
                       round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
            classy    = levels(glbObsTrn[, "classy"   ])[
                       round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
            kids      = levels(glbObsTrn[, "kids"     ])[
                       round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
                      )
    
    print("ObsNew output class tables:")
    print(sapply(c("lunch","dinner","reserve","outdoor",
                   "expensive","liquor","table",
                   "classy","kids"), 
                 function(feat) table(txfout_df[, feat], useNA = "ifany")))
    
    txfout_df <- txfout_df %>%
        dplyr::mutate(labels = "") %>%
        dplyr::mutate(labels = 
    ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
        dplyr::mutate(labels = 
    ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
        dplyr::mutate(labels = 
    ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
        dplyr::mutate(labels = 
    ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
        dplyr::mutate(labels =         
    ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
        dplyr::mutate(labels =         
    ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
        dplyr::mutate(labels =         
    ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
        dplyr::mutate(labels =         
    ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
        dplyr::mutate(labels =         
    ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
        dplyr::select(business_id, labels)
    return(txfout_df)
}
#if (!is.null(glbObsOut$mapFn)) obsout_df <- glbObsOut$mapFn(obsout_df); print(head(obsout_df))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
#     glbObsOut$vars[["Proba.Y"]] <- 
#         "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob]" 
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]" 
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "YelpRest_resXY_outdoor_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk 
#mysavChunk(glbOut$pfx, glbChunks[["last"]])

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("<scriptName>_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, "import.data")
##         label step_major step_minor label_minor    bgn end elapsed
## 1 import.data          1          0           0 24.223  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/train_resXY.csv..."
## [1] "dimensions of data in ./data/train_resXY.csv: 2,000 rows x 5 cols"
## [1] "   Truncating resXLst to first 100 chars..."
## [1] "   Truncating resYLst to first 100 chars..."
##   business_id        labels nImgs
## 1        1000 1 2 3 4 5 6 7    54
## 2        1001       0 1 6 8     9
## 3         100   1 2 4 5 6 7    84
## 4        1006     1 2 4 5 6    22
## 5        1010         0 6 8    11
## 6         101   1 2 3 4 5 6   121
##                                                                                                resXLst
## 1 500,375,375,375,375,375,500,500,500,500,500,500,500,500,375,414,373,500,399,375,375,375,500,500,472,
## 2                                                                  500,375,500,500,500,366,358,444,500
## 3 500,375,375,375,375,500,375,375,500,375,373,375,375,500,375,500,500,500,500,375,375,375,375,375,375,
## 4              500,373,281,500,500,500,500,500,500,500,500,396,500,500,500,281,281,375,375,375,375,375
## 5                                                          375,500,375,500,500,500,500,375,500,500,500
## 6 375,299,299,299,299,299,299,373,373,373,373,500,500,408,500,500,500,500,375,500,373,500,500,375,375,
##                                                                                                resYLst
## 1 500,500,500,500,500,500,332,332,332,332,332,375,375,375,500,500,500,389,500,500,500,500,375,375,500,
## 2                                                                  375,500,375,361,375,500,500,479,373
## 3 375,500,500,500,500,375,500,500,268,500,500,500,500,375,500,375,375,375,375,500,500,500,500,500,500,
## 4              375,500,500,273,375,375,375,375,375,399,290,500,500,500,375,500,500,500,500,500,500,500
## 5                                                          500,375,500,375,375,375,375,500,375,375,375
## 6 500,500,500,500,500,500,500,500,500,500,500,282,282,306,388,375,375,375,500,373,500,348,386,500,500,
##      business_id        labels nImgs
## 69          1102           6 8    37
## 305         1479         0 3 8   306
## 1019        2829       0 2 3 8   104
## 1455        3650             8    42
## 1468        3675 1 2 3 4 5 6 7    32
## 1978         959       3 5 6 8    29
##                                                                                                   resXLst
## 69   375,500,375,500,375,373,500,452,468,500,500,500,500,500,375,500,500,500,500,500,500,500,373,500,500,
## 305  500,373,373,500,500,500,500,373,375,500,500,500,500,373,373,373,375,500,375,373,373,375,373,500,500,
## 1019 500,375,375,500,500,375,375,375,500,373,375,375,500,375,500,375,375,375,374,375,500,375,375,500,375,
## 1455 375,375,375,375,375,375,375,500,375,500,500,375,375,375,500,375,500,375,500,373,375,500,500,375,281,
## 1468 156,375,500,500,500,500,500,500,500,375,375,375,500,500,500,375,500,375,375,500,500,375,441,500,433,
## 1978 500,500,375,500,500,374,373,373,373,500,500,373,500,375,500,500,375,375,500,375,375,500,375,500,500,
##                                                                                                   resYLst
## 69   500,375,500,375,500,500,373,500,500,375,375,375,375,332,500,375,375,375,375,375,375,373,500,373,500,
## 305  373,500,500,373,500,500,500,500,500,375,375,375,375,500,500,500,500,299,500,500,500,500,500,331,375,
## 1019 500,500,500,375,375,500,500,500,375,500,500,500,375,500,375,500,500,500,500,500,500,500,500,375,500,
## 1455 500,500,500,500,500,500,500,375,500,375,281,500,500,500,376,500,376,500,375,500,500,376,376,500,500,
## 1468 121,500,340,500,375,375,375,375,375,500,500,500,375,375,375,500,375,500,500,375,283,500,500,294,500,
## 1978 373,373,500,375,375,500,500,500,500,375,373,500,281,500,375,375,500,500,375,500,500,373,500,375,375,
##      business_id      labels nImgs
## 1995          99 1 2 4 5 6 7   139
## 1996         991 1 2 3 5 6 7    84
## 1997         993       3 6 8    34
## 1998         997           8   107
## 1999         998 1 2 4 5 6 7   320
## 2000         999   1 2 5 6 7    33
##                                                                                                   resXLst
## 1995 500,500,500,500,373,373,500,500,500,500,500,500,500,500,375,500,500,375,500,375,375,375,375,500,375,
## 1996 500,373,500,375,375,375,375,375,375,375,415,500,500,352,480,375,500,500,281,500,500,500,500,500,500,
## 1997 500,500,500,500,500,299,500,500,375,500,500,500,375,375,375,375,375,500,500,500,375,500,500,373,500,
## 1998 320,376,375,500,500,500,467,467,500,500,500,500,500,500,500,500,500,500,500,500,282,375,500,500,500,
## 1999 500,500,500,375,500,500,500,500,500,375,500,500,500,500,500,375,500,500,500,500,375,375,375,375,375,
## 2000 375,500,500,500,500,500,500,500,375,375,375,299,232,500,500,500,500,500,500,500,500,500,375,500,500,
##                                                                                                   resYLst
## 1995 375,500,500,500,500,500,375,375,375,375,375,375,375,375,500,375,375,500,375,500,500,500,500,500,500,
## 1996 500,500,373,500,500,500,500,500,500,500,499,387,375,500,360,500,375,500,500,375,373,373,373,373,373,
## 1997 373,375,375,375,299,500,299,375,500,375,375,375,500,500,500,500,500,373,280,280,500,281,373,500,373,
## 1998 240,500,500,414,375,375,351,351,373,373,373,336,336,336,336,343,336,337,337,343,500,500,500,500,500,
## 1999 450,279,281,500,375,375,373,282,375,500,375,340,375,375,375,500,373,373,373,373,500,500,500,500,500,
## 2000 500,375,375,375,375,375,375,375,500,500,500,500,64,281,281,281,375,375,375,375,375,375,500,500,500,5
## 'data.frame':    2000 obs. of  5 variables:
##  $ business_id: int  1000 1001 100 1006 1010 101 1011 1012 1014 1015 ...
##  $ labels     : chr  "1 2 3 4 5 6 7" "0 1 6 8" "1 2 4 5 6 7" "1 2 4 5 6" ...
##  $ nImgs      : int  54 9 84 22 11 121 70 37 32 145 ...
##  $ resXLst    : chr  "500,375,375,375,375,375,500,500,500,500,500,500,500,500,375,414,373,500,399,375,375,375,500,500,472,478,467,470,375,373,375,375"| __truncated__ "500,375,500,500,500,366,358,444,500" "500,375,375,375,375,500,375,375,500,375,373,375,375,500,375,500,500,500,500,375,375,375,375,375,375,375,375,373,373,375,375,375"| __truncated__ "500,373,281,500,500,500,500,500,500,500,500,396,500,500,500,281,281,375,375,375,375,375" ...
##  $ resYLst    : chr  "500,500,500,500,500,500,332,332,332,332,332,375,375,375,500,500,500,389,500,500,500,500,375,375,500,500,500,499,500,500,500,500"| __truncated__ "375,500,375,361,375,500,500,479,373" "375,500,500,500,500,375,500,500,268,500,500,500,500,375,500,375,375,375,375,500,500,500,500,500,500,500,500,500,500,500,500,500"| __truncated__ "375,500,500,273,375,375,375,375,375,399,290,500,500,500,375,500,500,500,500,500,500,500" ...
##  - attr(*, "comment")= chr "glbObsTrn"
## NULL
## [1] "Reading file ./data/test_resXY.csv..."
## [1] "dimensions of data in ./data/test_resXY.csv: 10,000 rows x 4 cols"
## [1] "   Truncating resXLst to first 100 chars..."
## [1] "   Truncating resYLst to first 100 chars..."
##   business_id nImgs
## 1       003sg   167
## 2       00er5   210
## 3       00kad    83
## 4       00mc6    15
## 5       00q7x    24
## 6       00v0t    24
##                                                                                                resXLst
## 1 375,500,375,375,500,375,500,500,500,500,500,281,500,500,500,373,375,500,375,373,375,500,500,375,500,
## 2 489,500,500,281,375,397,469,500,320,375,500,375,500,375,375,375,500,500,345,375,375,500,500,500,281,
## 3 332,500,500,375,281,375,500,500,500,375,500,500,375,375,375,500,500,500,500,375,500,500,375,500,500,
## 4                                          375,500,500,375,323,500,500,500,281,500,375,500,500,500,375
## 5      500,500,373,375,500,500,500,500,375,500,375,375,325,500,375,500,500,500,500,375,500,375,375,500
## 6      375,500,375,500,375,500,500,500,500,373,500,375,375,375,500,500,500,500,375,500,500,500,375,375
##                                                                                                resYLst
## 1 500,375,500,500,500,500,375,375,350,375,500,500,375,375,373,500,500,500,500,500,500,332,375,500,375,
## 2 500,500,375,500,500,500,314,375,480,500,375,500,375,500,500,500,373,375,500,500,500,375,380,282,500,
## 3 500,375,375,500,500,500,281,281,281,500,375,500,500,500,500,375,375,374,335,500,373,344,500,373,375,
## 4                                          500,500,375,500,500,375,418,375,500,375,500,375,500,333,500
## 5      375,500,500,500,375,375,372,333,500,375,500,500,500,375,500,375,375,375,375,500,373,500,500,500
## 6      500,375,500,375,500,375,375,375,281,500,375,500,500,500,500,375,375,375,500,500,375,282,500,500
##      business_id nImgs
## 12         01mrb    62
## 1789       6ey8p    40
## 3881       dqqme   117
## 3912       dv9lg    15
## 4024       ebyno   128
## 4625       gkb3z    44
##                                                                                                   resXLst
## 12   500,500,500,500,500,375,500,500,373,500,500,500,375,375,375,333,500,375,375,500,500,375,332,500,464,
## 1789 500,373,375,500,500,375,500,500,375,500,375,500,360,500,500,500,375,375,500,500,500,500,500,350,500,
## 3881 500,500,375,500,500,375,500,375,375,500,375,500,500,281,375,375,376,500,500,375,375,500,500,281,375,
## 3912                                          281,500,500,281,375,500,500,362,500,500,375,500,500,281,500
## 4024 375,375,375,500,372,375,500,500,373,500,375,375,500,500,373,282,375,500,500,281,375,375,500,500,375,
## 4625 375,500,500,500,375,500,500,500,500,500,500,500,375,500,500,500,373,375,375,375,375,500,500,375,500,
##                                                                                                   resYLst
## 12   375,333,375,375,375,500,375,375,500,375,375,334,500,500,500,500,375,500,500,500,375,500,500,375,368,
## 1789 375,500,500,333,500,500,373,500,500,333,500,375,450,375,375,375,500,500,373,375,374,375,375,263,373,
## 3881 334,433,500,375,375,500,375,500,500,500,500,299,375,500,500,500,500,375,375,500,500,375,500,500,500,
## 3912                                          500,375,375,500,500,375,373,500,375,375,500,375,375,500,375
## 4024 500,500,500,375,500,500,281,500,500,375,500,500,500,319,500,500,500,375,375,500,500,500,375,375,500,
## 4625 500,375,375,375,500,375,375,375,373,469,373,373,500,442,413,373,500,500,500,500,500,375,375,500,375,
##       business_id nImgs
## 9995        zyrif    89
## 9996        zyvg6    16
## 9997        zyvjj    27
## 9998        zz8g4   118
## 9999        zzxkg   154
## 10000       zzxwm    13
##                                                                                                    resXLst
## 9995  375,500,375,500,500,500,375,375,500,500,375,375,500,375,375,500,500,281,281,500,500,375,375,500,500,
## 9996                                       500,500,375,373,500,500,500,375,500,375,500,375,280,375,500,375
## 9997  500,375,500,500,500,500,500,402,500,373,500,375,500,500,500,500,375,500,500,375,500,375,500,500,281,
## 9998  375,500,375,500,375,375,375,500,500,375,500,500,500,500,500,499,500,500,500,375,282,500,500,500,375,
## 9999  500,500,500,500,375,500,500,500,375,375,375,299,500,500,375,500,500,375,500,500,500,373,500,281,500,
## 10000                                                  500,373,500,281,500,375,333,375,375,218,500,500,500
##                                                                                                    resYLst
## 9995  500,375,500,375,375,375,500,500,500,281,500,500,375,500,500,375,375,500,500,373,375,500,500,500,373,
## 9996                                       375,375,500,500,375,500,375,500,375,500,375,500,500,500,375,500
## 9997  373,500,375,375,406,373,373,315,373,500,375,500,281,373,375,375,500,280,373,500,375,500,375,375,500,
## 9998  500,375,500,232,500,500,500,375,375,500,375,375,375,373,500,323,334,373,375,500,500,335,375,280,500,
## 9999  375,375,375,375,500,373,375,375,500,500,500,500,375,500,500,375,371,500,281,375,375,500,375,500,375,
## 10000                                                  281,500,299,500,374,500,500,500,500,211,375,375,281
## 'data.frame':    10000 obs. of  4 variables:
##  $ business_id: chr  "003sg" "00er5" "00kad" "00mc6" ...
##  $ nImgs      : int  167 210 83 15 24 24 40 10 49 10 ...
##  $ resXLst    : chr  "375,500,375,375,500,375,500,500,500,500,500,281,500,500,500,373,375,500,375,373,375,500,500,375,500,500,375,279,500,375,500,500"| __truncated__ "489,500,500,281,375,397,469,500,320,375,500,375,500,375,375,375,500,500,345,375,375,500,500,500,281,373,500,375,500,375,500,375"| __truncated__ "332,500,500,375,281,375,500,500,500,375,500,500,375,375,375,500,500,500,500,375,500,500,375,500,500,500,500,432,281,373,500,297"| __truncated__ "375,500,500,375,323,500,500,500,281,500,375,500,500,500,375" ...
##  $ resYLst    : chr  "500,375,500,500,500,500,375,375,350,375,500,500,375,375,373,500,500,500,500,500,500,332,375,500,375,375,500,500,375,500,375,500"| __truncated__ "500,500,375,500,500,500,314,375,480,500,375,500,375,500,500,500,373,375,500,500,500,375,380,282,500,500,281,500,361,500,375,500"| __truncated__ "500,375,375,500,500,500,281,281,281,500,375,500,500,500,500,375,375,374,335,500,373,344,500,373,375,375,333,500,500,500,299,500"| __truncated__ "500,500,375,500,500,375,418,375,500,375,500,375,500,333,500" ...
##  - attr(*, "comment")= chr "glbObsNew"
## NULL
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: nImgs.log1p..."
## [1] "Creating new feature: nImgs.root2..."
## [1] "Creating new feature: nImgs.nexp..."
## [1] "Creating new feature: resX.min..."
## [1] "Creating new feature: resX.max..."
## [1] "Creating new feature: resX.mean..."
## [1] "Creating new feature: resX.mad..."
## [1] "Creating new feature: resX.min.log1p..."
## [1] "Creating new feature: resX.min.root2..."
## [1] "Creating new feature: resX.min.nexp..."
## [1] "Creating new feature: resX.max.log1p..."
## [1] "Creating new feature: resX.max.root2..."
## [1] "Creating new feature: resX.max.nexp..."
## [1] "Creating new feature: resX.mean.log1p..."
## [1] "Creating new feature: resX.mean.root2..."
## [1] "Creating new feature: resX.mean.nexp..."
## [1] "Creating new feature: resX.mad.log1p..."
## [1] "Creating new feature: resX.mad.root2..."
## [1] "Creating new feature: resX.mad.nexp..."
## [1] "Creating new feature: resY.min..."
## [1] "Creating new feature: resY.max..."
## [1] "Creating new feature: resY.mean..."
## [1] "Creating new feature: resY.mad..."
## [1] "Creating new feature: resY.min.log1p..."
## [1] "Creating new feature: resY.min.root2..."
## [1] "Creating new feature: resY.min.nexp..."
## [1] "Creating new feature: resY.max.log1p..."
## [1] "Creating new feature: resY.max.root2..."
## [1] "Creating new feature: resY.max.nexp..."
## [1] "Creating new feature: resY.mean.log1p..."
## [1] "Creating new feature: resY.mean.root2..."
## [1] "Creating new feature: resY.mean.nexp..."
## [1] "Creating new feature: resY.mad.log1p..."
## [1] "Creating new feature: resY.mad.root2..."
## [1] "Creating new feature: resY.mad.nexp..."
## [1] "Creating new feature: resXY.min..."
## [1] "Creating new feature: resXY.max..."
## [1] "Creating new feature: resXY.mean..."
## [1] "Creating new feature: resXY.mad..."
## [1] "Creating new feature: resXY.min.log1p..."
## [1] "Creating new feature: resXY.min.root2..."
## [1] "Creating new feature: resXY.min.nexp..."
## [1] "Creating new feature: resXY.max.log1p..."
## [1] "Creating new feature: resXY.max.root2..."
## [1] "Creating new feature: resXY.max.nexp..."
## [1] "Creating new feature: resXY.mean.log1p..."
## [1] "Creating new feature: resXY.mean.root2..."
## [1] "Creating new feature: resXY.mean.nexp..."
## [1] "Creating new feature: resXY.mad.log1p..."
## [1] "Creating new feature: resXY.mad.root2..."
## [1] "Creating new feature: resXY.mad.nexp..."
## [1] "Creating new feature: lunch..."
## [1] "Creating new feature: dinner..."
## [1] "Creating new feature: reserve..."
## [1] "Creating new feature: outdoor..."
## [1] "Creating new feature: expensive..."
## [1] "Creating new feature: liquor..."
## [1] "Creating new feature: table..."
## [1] "Creating new feature: classy..."
## [1] "Creating new feature: kids..."
## [1] "Creating new feature: nImgs.cut.fctr..."
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##   outdoor  .src    .n
## 1    <NA>  Test 10000
## 2       3 Train  1003
## 3      -1 Train   997
##   outdoor  .src    .n
## 1    <NA>  Test 10000
## 2       3 Train  1003
## 3      -1 Train   997
## Loading required package: RColorBrewer

##    .src    .n
## 1  Test 10000
## 2 Train  2000
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor    bgn    end elapsed
## 1  import.data          1          0           0 24.223 79.635  55.412
## 2 inspect.data          2          0           0 79.636     NA      NA

Step 2.0: inspect data

## Loading required package: reshape2

##       outdoor.-1 outdoor.3 outdoor.NA
## Test          NA        NA      10000
## Train        997      1003         NA
##       outdoor.-1 outdoor.3 outdoor.NA
## Test          NA        NA          1
## Train     0.4985    0.5015         NA
## [1] "numeric data missing in glbObsAll: "
##     lunch    dinner   reserve   outdoor expensive    liquor     table 
##     10000     10000     10000     10000     10000     10000     10000 
##    classy      kids 
##     10000     10000 
## [1] "numeric data w/ 0s in glbObsAll: "
##      nImgs.nexp        resX.mad  resX.mad.log1p  resX.mad.root2 
##             228            9353            9353            9353 
##        resY.mad  resY.mad.log1p  resY.mad.root2       resXY.mad 
##            5442            5442            5442           10915 
##  resXY.min.nexp  resXY.max.nexp resXY.mean.nexp resXY.mad.log1p 
##           12000           12000           12000           10915 
## resXY.mad.root2  resXY.mad.nexp           lunch 
##           10915             850             671 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## business_id      labels     resXLst     resYLst 
##           0          NA           0           0
##   outdoor outdoor.fctr    .n
## 1    <NA>         <NA> 10000
## 2       3            Y  1003
## 3      -1            N   997
## Warning: Removed 1 rows containing missing values (position_stack).

##       outdoor.fctr.N outdoor.fctr.Y outdoor.fctr.NA
## Test              NA             NA           10000
## Train            997           1003              NA
##       outdoor.fctr.N outdoor.fctr.Y outdoor.fctr.NA
## Test              NA             NA               1
## Train         0.4985         0.5015              NA

## NULL
##          label step_major step_minor label_minor     bgn     end elapsed
## 2 inspect.data          2          0           0  79.636 116.109  36.474
## 3   scrub.data          2          1           1 116.110      NA      NA

Step 2.1: scrub data

## [1] "numeric data missing in glbObsAll: "
##        lunch       dinner      reserve      outdoor    expensive 
##        10000        10000        10000        10000        10000 
##       liquor        table       classy         kids outdoor.fctr 
##        10000        10000        10000        10000        10000 
## [1] "numeric data w/ 0s in glbObsAll: "
##      nImgs.nexp        resX.mad  resX.mad.log1p  resX.mad.root2 
##             228            9353            9353            9353 
##        resY.mad  resY.mad.log1p  resY.mad.root2       resXY.mad 
##            5442            5442            5442           10915 
##  resXY.min.nexp  resXY.max.nexp resXY.mean.nexp resXY.mad.log1p 
##           12000           12000           12000           10915 
## resXY.mad.root2  resXY.mad.nexp           lunch 
##           10915             850             671 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## business_id      labels     resXLst     resYLst 
##           0          NA           0           0
##            label step_major step_minor label_minor     bgn     end elapsed
## 3     scrub.data          2          1           1 116.110 126.864  10.754
## 4 transform.data          2          2           2 126.865      NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor     bgn     end
## 4   transform.data          2          2           2 126.865 126.905
## 5 extract.features          3          0           0 126.905      NA
##   elapsed
## 4    0.04
## 5      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor     bgn
## 5          extract.features          3          0           0 126.905
## 6 extract.features.datetime          3          1           1 126.926
##       end elapsed
## 5 126.926   0.021
## 6      NA      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor     bgn
## 1 extract.features.datetime.bgn          1          0           0 126.977
##   end elapsed
## 1  NA      NA
##                       label step_major step_minor label_minor     bgn
## 6 extract.features.datetime          3          1           1 126.926
## 7    extract.features.image          3          2           2 126.988
##       end elapsed
## 6 126.987   0.061
## 7      NA      NA

Step 3.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.image.bgn          1          0           0 127.029  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 127.029
## 2 extract.features.image.end          2          0           0 127.039
##       end elapsed
## 1 127.038    0.01
## 2      NA      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 127.029
## 2 extract.features.image.end          2          0           0 127.039
##       end elapsed
## 1 127.038    0.01
## 2      NA      NA
##                    label step_major step_minor label_minor     bgn     end
## 7 extract.features.image          3          2           2 126.988 127.048
## 8 extract.features.price          3          3           3 127.049      NA
##   elapsed
## 7    0.06
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.price.bgn          1          0           0 127.074  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor     bgn     end
## 8 extract.features.price          3          3           3 127.049 127.083
## 9  extract.features.text          3          4           4 127.083      NA
##   elapsed
## 8   0.034
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor    bgn end
## 1 extract.features.text.bgn          1          0           0 127.14  NA
##   elapsed
## 1      NA
##                      label step_major step_minor label_minor     bgn
## 9    extract.features.text          3          4           4 127.083
## 10 extract.features.string          3          5           5 127.150
##        end elapsed
## 9  127.149   0.066
## 10      NA      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor    bgn end
## 1 extract.features.string.bgn          1          0           0 127.18  NA
##   elapsed
## 1      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor     bgn     end elapsed
## 1           0 127.180 127.189   0.009
## 2           0 127.189      NA      NA
##   business_id        labels       resXLst       resYLst          .src 
## "business_id"      "labels"     "resXLst"     "resYLst"        ".src"
##                      label step_major step_minor label_minor     bgn
## 10 extract.features.string          3          5           5 127.150
## 11    extract.features.end          3          6           6 127.205
##        end elapsed
## 10 127.204   0.054
## 11      NA      NA

Step 3.6: extract features end

## [1] "Summary for lunch:"
##        
##            -1     0  <NA>
##   Test      0     0 10000
##   Train  1329   671     0
## [1] "Summary for dinner:"
##        
##            -1     1  <NA>
##   Test      0     0 10000
##   Train  1007   993     0
## [1] "Summary for reserve:"
##        
##            -1     2  <NA>
##   Test      0     0 10000
##   Train   974  1026     0
## [1] "Summary for outdoor:"
##        
##            -1     3  <NA>
##   Test      0     0 10000
##   Train   997  1003     0
## [1] "Summary for expensive:"
##        
##            -1     4  <NA>
##   Test      0     0 10000
##   Train  1453   547     0
## [1] "Summary for liquor:"
##        
##            -1     5  <NA>
##   Test      0     0 10000
##   Train   751  1249     0
## [1] "Summary for table:"
##        
##            -1     6  <NA>
##   Test      0     0 10000
##   Train   640  1360     0
## [1] "Summary for classy:"
##        
##            -1     7  <NA>
##   Test      0     0 10000
##   Train  1428   572     0
## [1] "Summary for kids:"
##        
##            -1     8  <NA>
##   Test      0     0 10000
##   Train   762  1238     0
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor     bgn     end
## 11 extract.features.end          3          6           6 127.205 128.134
## 12  manage.missing.data          4          0           0 128.135      NA
##    elapsed
## 11   0.929
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in glbObsAll: "
##        lunch       dinner      reserve      outdoor    expensive 
##        10000        10000        10000        10000        10000 
##       liquor        table       classy         kids outdoor.fctr 
##        10000        10000        10000        10000        10000 
## [1] "numeric data w/ 0s in glbObsAll: "
##      nImgs.nexp        resX.mad  resX.mad.log1p  resX.mad.root2 
##             228            9353            9353            9353 
##        resY.mad  resY.mad.log1p  resY.mad.root2       resXY.mad 
##            5442            5442            5442           10915 
##  resXY.min.nexp  resXY.max.nexp resXY.mean.nexp resXY.mad.log1p 
##           12000           12000           12000           10915 
## resXY.mad.root2  resXY.mad.nexp           lunch 
##           10915             850             671 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## business_id      labels     resXLst     resYLst 
##           0          NA           0           0
## [1] "numeric data missing in glbObsAll: "
##        lunch       dinner      reserve      outdoor    expensive 
##        10000        10000        10000        10000        10000 
##       liquor        table       classy         kids outdoor.fctr 
##        10000        10000        10000        10000        10000 
## [1] "numeric data w/ 0s in glbObsAll: "
##      nImgs.nexp        resX.mad  resX.mad.log1p  resX.mad.root2 
##             228            9353            9353            9353 
##        resY.mad  resY.mad.log1p  resY.mad.root2       resXY.mad 
##            5442            5442            5442           10915 
##  resXY.min.nexp  resXY.max.nexp resXY.mean.nexp resXY.mad.log1p 
##           12000           12000           12000           10915 
## resXY.mad.root2  resXY.mad.nexp           lunch 
##           10915             850             671 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## business_id      labels     resXLst     resYLst 
##           0          NA           0           0
##                  label step_major step_minor label_minor     bgn     end
## 12 manage.missing.data          4          0           0 128.135 128.566
## 13        cluster.data          5          0           0 128.567      NA
##    elapsed
## 12   0.431
## 13      NA

Step 5.0: cluster data

##                      label step_major step_minor label_minor     bgn
## 13            cluster.data          5          0           0 128.567
## 14 partition.data.training          6          0           0 128.635
##        end elapsed
## 13 128.635   0.068
## 14      NA      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.15 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.15 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following objects are masked from 'package:survival':
## 
##     cluster, strata
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 0.41 secs"
##     outdoor.-1 outdoor.3 outdoor.NA
##             NA        NA      10000
## Fit        500       502         NA
## OOB        497       501         NA
##     outdoor.-1 outdoor.3 outdoor.NA
##             NA        NA          1
## Fit   0.499002  0.500998         NA
## OOB   0.497996  0.502004         NA
##   nImgs.cut.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1         (0,32]    238    237   2532      0.2375250      0.2374749
## 3        (32,60]    278    243   2512      0.2774451      0.2434870
## 2    (120,3e+03]    229    258   2497      0.2285429      0.2585170
## 4       (60,120]    257    260   2459      0.2564870      0.2605210
##   .freqRatio.Tst
## 1         0.2532
## 3         0.2512
## 2         0.2497
## 4         0.2459
## [1] "glbObsAll: "
## [1] 12000    71
## [1] "glbObsTrn: "
## [1] 2000   71
## [1] "glbObsFit: "
## [1] 1002   70
## [1] "glbObsOOB: "
## [1] 998  70
## [1] "glbObsNew: "
## [1] 10000    70
## [1] "partition.data.training chunk: teardown: elapsed: 1.23 secs"
##                      label step_major step_minor label_minor     bgn
## 14 partition.data.training          6          0           0 128.635
## 15         select.features          7          0           0 129.924
##        end elapsed
## 14 129.923   1.289
## 15      NA      NA

Step 7.0: select features

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
## [1] "cor(resX.max, resX.max.log1p)=1.0000"
## [1] "cor(outdoor.fctr, resX.max)=-0.0315"
## [1] "cor(outdoor.fctr, resX.max.log1p)=-0.0315"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.max.log1p as highly correlated with resX.max
## [1] "cor(resX.max, resX.max.nexp)=-1.0000"
## [1] "cor(outdoor.fctr, resX.max)=-0.0315"
## [1] "cor(outdoor.fctr, resX.max.nexp)=0.0315"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.max as highly correlated with resX.max.nexp
## [1] "cor(resX.max.nexp, resX.max.root2)=-1.0000"
## [1] "cor(outdoor.fctr, resX.max.nexp)=0.0315"
## [1] "cor(outdoor.fctr, resX.max.root2)=-0.0315"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.max.root2 as highly correlated with
## resX.max.nexp
## [1] "cor(resX.mean.nexp, resY.mean.nexp)=1.0000"
## [1] "cor(outdoor.fctr, resX.mean.nexp)=-0.0224"
## [1] "cor(outdoor.fctr, resY.mean.nexp)=-0.0224"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.mean.nexp as highly correlated with
## resX.mean.nexp
## [1] "cor(resY.max, resY.max.root2)=1.0000"
## [1] "cor(outdoor.fctr, resY.max)=0.0117"
## [1] "cor(outdoor.fctr, resY.max.root2)=0.0116"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.max.root2 as highly correlated with resY.max
## [1] "cor(resY.max, resY.max.log1p)=1.0000"
## [1] "cor(outdoor.fctr, resY.max)=0.0117"
## [1] "cor(outdoor.fctr, resY.max.log1p)=0.0115"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.max.log1p as highly correlated with resY.max
## [1] "cor(resX.mean, resX.mean.root2)=0.9996"
## [1] "cor(outdoor.fctr, resX.mean)=-0.0177"
## [1] "cor(outdoor.fctr, resX.mean.root2)=-0.0164"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.mean.root2 as highly correlated with resX.mean
## [1] "cor(resY.mean, resY.mean.root2)=0.9995"
## [1] "cor(outdoor.fctr, resY.mean)=0.0126"
## [1] "cor(outdoor.fctr, resY.mean.root2)=0.0131"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.mean as highly correlated with resY.mean.root2
## [1] "cor(resY.mean.log1p, resY.mean.root2)=0.9994"
## [1] "cor(outdoor.fctr, resY.mean.log1p)=0.0136"
## [1] "cor(outdoor.fctr, resY.mean.root2)=0.0131"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.mean.root2 as highly correlated with
## resY.mean.log1p
## [1] "cor(resX.mean, resX.mean.log1p)=0.9985"
## [1] "cor(outdoor.fctr, resX.mean)=-0.0177"
## [1] "cor(outdoor.fctr, resX.mean.log1p)=-0.0151"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.mean.log1p as highly correlated with resX.mean
## [1] "cor(resX.min, resX.min.root2)=0.9940"
## [1] "cor(outdoor.fctr, resX.min)=-0.0314"
## [1] "cor(outdoor.fctr, resX.min.root2)=-0.0303"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.min.root2 as highly correlated with resX.min
## [1] "cor(resY.min, resY.min.root2)=0.9935"
## [1] "cor(outdoor.fctr, resY.min)=-0.0509"
## [1] "cor(outdoor.fctr, resY.min.root2)=-0.0474"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.min.root2 as highly correlated with resY.min
## [1] "cor(resX.mad.log1p, resX.mad.root2)=0.9880"
## [1] "cor(outdoor.fctr, resX.mad.log1p)=0.0220"
## [1] "cor(outdoor.fctr, resX.mad.root2)=0.0219"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.mad.root2 as highly correlated with
## resX.mad.log1p
## [1] "cor(resXY.min, resXY.min.root2)=0.9872"
## [1] "cor(outdoor.fctr, resXY.min)=-0.0495"
## [1] "cor(outdoor.fctr, resXY.min.root2)=-0.0414"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resXY.min.root2 as highly correlated with resXY.min
## [1] "cor(resXY.mad.log1p, resXY.mad.nexp)=-0.9803"
## [1] "cor(outdoor.fctr, resXY.mad.log1p)=-0.0141"
## [1] "cor(outdoor.fctr, resXY.mad.nexp)=0.0154"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resXY.mad.log1p as highly correlated with
## resXY.mad.nexp
## [1] "cor(resX.min, resX.min.log1p)=0.9751"
## [1] "cor(outdoor.fctr, resX.min)=-0.0314"
## [1] "cor(outdoor.fctr, resX.min.log1p)=-0.0301"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.min.log1p as highly correlated with resX.min
## [1] "cor(resY.min, resY.min.log1p)=0.9718"
## [1] "cor(outdoor.fctr, resY.min)=-0.0509"
## [1] "cor(outdoor.fctr, resY.min.log1p)=-0.0431"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resY.min.log1p as highly correlated with resY.min
## [1] "cor(resX.mad, resX.mad.log1p)=0.9375"
## [1] "cor(outdoor.fctr, resX.mad)=0.0205"
## [1] "cor(outdoor.fctr, resX.mad.log1p)=0.0220"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.mad as highly correlated with resX.mad.log1p
## [1] "cor(resXY.min, resXY.min.log1p)=0.9357"
## [1] "cor(outdoor.fctr, resXY.min)=-0.0495"
## [1] "cor(outdoor.fctr, resXY.min.log1p)=-0.0338"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resXY.min.log1p as highly correlated with resXY.min
## [1] "cor(resXY.mad, resXY.mad.root2)=0.9334"
## [1] "cor(outdoor.fctr, resXY.mad)=-0.0119"
## [1] "cor(outdoor.fctr, resXY.mad.root2)=-0.0114"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resXY.mad.root2 as highly correlated with resXY.mad
## [1] "cor(resX.mad.log1p, resX.mad.nexp)=-0.9321"
## [1] "cor(outdoor.fctr, resX.mad.log1p)=0.0220"
## [1] "cor(outdoor.fctr, resX.mad.nexp)=-0.0140"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resX.mad.nexp as highly correlated with
## resX.mad.log1p
## [1] "cor(nImgs.log1p, nImgs.root2)=0.9280"
## [1] "cor(outdoor.fctr, nImgs.log1p)=0.0473"
## [1] "cor(outdoor.fctr, nImgs.root2)=0.0140"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified nImgs.root2 as highly correlated with nImgs.log1p
## [1] "cor(nImgs.cut.fctr, nImgs.log1p)=0.9109"
## [1] "cor(outdoor.fctr, nImgs.cut.fctr)=0.0586"
## [1] "cor(outdoor.fctr, nImgs.log1p)=0.0473"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glbObsTrn, : Identified nImgs.log1p as highly correlated with
## nImgs.cut.fctr
## [1] "cor(resXY.min, resY.min)=0.8858"
## [1] "cor(outdoor.fctr, resXY.min)=-0.0495"
## [1] "cor(outdoor.fctr, resY.min)=-0.0509"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified resXY.min as highly correlated with resY.min
##                         cor.y exclude.as.feat   cor.y.abs      cor.high.X
## outdoor           1.000000000               1 1.000000000            <NA>
## liquor            0.100416198               1 0.100416198            <NA>
## nImgs.cut.fctr    0.058567974               0 0.058567974            <NA>
## nImgs.log1p       0.047250893               0 0.047250893  nImgs.cut.fctr
## reserve           0.038935338               1 0.038935338            <NA>
## resX.max.nexp     0.031543826               0 0.031543826            <NA>
## .pos              0.027497300               0 0.027497300            <NA>
## resX.mad.log1p    0.022032870               0 0.022032870            <NA>
## resX.mad.root2    0.021937317               0 0.021937317  resX.mad.log1p
## resX.mad          0.020537518               0 0.020537518  resX.mad.log1p
## resY.max.nexp     0.019090479               0 0.019090479            <NA>
## expensive         0.017228141               1 0.017228141            <NA>
## classy            0.015804825               1 0.015804825            <NA>
## resXY.mad.nexp    0.015437895               0 0.015437895            <NA>
## nImgs.root2       0.014028124               0 0.014028124     nImgs.log1p
## resY.mean.log1p   0.013625190               0 0.013625190            <NA>
## resY.mean.root2   0.013106506               0 0.013106506 resY.mean.log1p
## resY.mean         0.012599188               0 0.012599188 resY.mean.root2
## resY.mad.nexp     0.012190340               0 0.012190340            <NA>
## resY.max          0.011656712               0 0.011656712            <NA>
## resY.max.root2    0.011556200               0 0.011556200        resY.max
## resY.max.log1p    0.011451372               0 0.011451372        resY.max
## resY.mad          0.007630633               0 0.007630633            <NA>
## resXY.max.log1p   0.005240654               0 0.005240654            <NA>
## resXY.max.root2   0.004944889               0 0.004944889            <NA>
## resXY.max         0.004653277               0 0.004653277            <NA>
## resY.mad.root2    0.002557583               0 0.002557583            <NA>
## resY.mad.log1p   -0.001526058               0 0.001526058            <NA>
## nImgs.nexp       -0.003435316               0 0.003435316            <NA>
## resXY.mean.log1p -0.004867571               0 0.004867571            <NA>
## lunch            -0.005308550               1 0.005308550            <NA>
## resXY.mean.root2 -0.007039955               0 0.007039955            <NA>
## .rnorm           -0.008042720               0 0.008042720            <NA>
## resXY.mean       -0.009002880               0 0.009002880            <NA>
## resXY.mad.root2  -0.011364822               0 0.011364822       resXY.mad
## resXY.mad        -0.011946049               0 0.011946049            <NA>
## resX.mad.nexp    -0.014000008               0 0.014000008  resX.mad.log1p
## resXY.mad.log1p  -0.014055066               0 0.014055066  resXY.mad.nexp
## nImgs            -0.014963676               0 0.014963676            <NA>
## resX.mean.log1p  -0.015059015               0 0.015059015       resX.mean
## resX.mean.root2  -0.016434019               0 0.016434019       resX.mean
## resX.mean        -0.017726551               0 0.017726551            <NA>
## resX.min.nexp    -0.022391602               0 0.022391602            <NA>
## resX.mean.nexp   -0.022433472               0 0.022433472            <NA>
## resY.mean.nexp   -0.022433472               0 0.022433472  resX.mean.nexp
## resY.min.nexp    -0.022433600               0 0.022433600            <NA>
## resX.min.log1p   -0.030103276               0 0.030103276        resX.min
## resX.min.root2   -0.030339745               0 0.030339745        resX.min
## resX.min         -0.031436275               0 0.031436275            <NA>
## resX.max         -0.031543826               0 0.031543826   resX.max.nexp
## resX.max.log1p   -0.031543826               0 0.031543826        resX.max
## resX.max.root2   -0.031543826               0 0.031543826   resX.max.nexp
## resXY.min.log1p  -0.033756424               0 0.033756424       resXY.min
## dinner           -0.039980159               1 0.039980159            <NA>
## resXY.min.root2  -0.041449898               0 0.041449898       resXY.min
## resY.min.log1p   -0.043072548               0 0.043072548        resY.min
## resY.min.root2   -0.047387777               0 0.047387777        resY.min
## resXY.min        -0.049458217               0 0.049458217        resY.min
## resY.min         -0.050925308               0 0.050925308            <NA>
## table            -0.055823041               1 0.055823041            <NA>
## kids             -0.075895168               1 0.075895168            <NA>
## resXY.max.nexp             NA               0          NA            <NA>
## resXY.mean.nexp            NA               0          NA            <NA>
## resXY.min.nexp             NA               0          NA            <NA>
##                    freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## outdoor             1.006018          0.10   FALSE FALSE            FALSE
## liquor              1.663116          0.10   FALSE FALSE            FALSE
## nImgs.cut.fctr      1.007737          0.20   FALSE FALSE            FALSE
## nImgs.log1p         1.033333         19.10   FALSE FALSE            FALSE
## reserve             1.053388          0.10   FALSE FALSE            FALSE
## resX.max.nexp     999.000000          0.10   FALSE  TRUE            FALSE
## .pos                1.000000        100.00   FALSE FALSE            FALSE
## resX.mad.log1p     11.209677          7.55   FALSE FALSE            FALSE
## resX.mad.root2     11.209677          7.55   FALSE FALSE            FALSE
## resX.mad           11.209677          7.55   FALSE FALSE            FALSE
## resY.max.nexp    1997.000000          0.20   FALSE  TRUE            FALSE
## expensive           2.656307          0.10   FALSE FALSE            FALSE
## classy              2.496503          0.10   FALSE FALSE            FALSE
## resXY.mad.nexp      4.841317          0.30   FALSE FALSE            FALSE
## nImgs.root2         1.033333         19.10   FALSE FALSE            FALSE
## resY.mean.log1p     1.666667         97.90   FALSE FALSE            FALSE
## resY.mean.root2     1.666667         97.85   FALSE FALSE            FALSE
## resY.mean           1.666667         98.15   FALSE FALSE            FALSE
## resY.mad.nexp       5.354497          9.05   FALSE FALSE            FALSE
## resY.max         1997.000000          0.20   FALSE  TRUE            FALSE
## resY.max.root2   1997.000000          0.20   FALSE  TRUE            FALSE
## resY.max.log1p   1997.000000          0.20   FALSE  TRUE            FALSE
## resY.mad            5.354497          9.05   FALSE FALSE             TRUE
## resXY.max.log1p     6.272358          5.45   FALSE FALSE             TRUE
## resXY.max.root2     6.272358          5.45   FALSE FALSE             TRUE
## resXY.max           6.272358          5.45   FALSE FALSE             TRUE
## resY.mad.root2      5.354497          9.05   FALSE FALSE             TRUE
## resY.mad.log1p      5.354497          9.05   FALSE FALSE             TRUE
## nImgs.nexp          1.193548         17.35   FALSE FALSE             TRUE
## resXY.mean.log1p    4.000000         90.80   FALSE FALSE             TRUE
## lunch               1.980626          0.10   FALSE FALSE             TRUE
## resXY.mean.root2    6.000000         98.20   FALSE FALSE             TRUE
## .rnorm              1.000000        100.00   FALSE FALSE            FALSE
## resXY.mean          6.000000         98.55   FALSE FALSE            FALSE
## resXY.mad.root2     9.568047          4.35   FALSE FALSE            FALSE
## resXY.mad           9.568047          4.35   FALSE FALSE            FALSE
## resX.mad.nexp      11.209677          7.55   FALSE FALSE            FALSE
## resXY.mad.log1p     9.568047          4.35   FALSE FALSE            FALSE
## nImgs               1.033333         19.10   FALSE FALSE            FALSE
## resX.mean.log1p     2.000000         97.60   FALSE FALSE            FALSE
## resX.mean.root2     2.000000         97.45   FALSE FALSE            FALSE
## resX.mean           2.000000         97.75   FALSE FALSE            FALSE
## resX.min.nexp       6.000000         11.45   FALSE FALSE            FALSE
## resX.mean.nexp      2.000000         97.75   FALSE FALSE            FALSE
## resY.mean.nexp      1.666667         98.15   FALSE FALSE            FALSE
## resY.min.nexp       9.824561         13.85   FALSE FALSE            FALSE
## resX.min.log1p      6.000000         11.45   FALSE FALSE            FALSE
## resX.min.root2      6.000000         11.45   FALSE FALSE            FALSE
## resX.min            6.000000         11.45   FALSE FALSE            FALSE
## resX.max          999.000000          0.10   FALSE  TRUE            FALSE
## resX.max.log1p    999.000000          0.10   FALSE  TRUE            FALSE
## resX.max.root2    999.000000          0.10   FALSE  TRUE            FALSE
## resXY.min.log1p     9.745455         37.65   FALSE FALSE            FALSE
## dinner              1.014099          0.10   FALSE FALSE            FALSE
## resXY.min.root2     9.745455         37.65   FALSE FALSE            FALSE
## resY.min.log1p      9.824561         13.85   FALSE FALSE            FALSE
## resY.min.root2      9.824561         13.85   FALSE FALSE            FALSE
## resXY.min           9.745455         37.65   FALSE FALSE            FALSE
## resY.min            9.824561         13.85   FALSE FALSE            FALSE
## table               2.125000          0.10   FALSE FALSE            FALSE
## kids                1.624672          0.10   FALSE FALSE            FALSE
## resXY.max.nexp      0.000000          0.05    TRUE  TRUE               NA
## resXY.mean.nexp     0.000000          0.05    TRUE  TRUE               NA
## resXY.min.nexp      0.000000          0.05    TRUE  TRUE               NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 16 rows containing missing values (geom_point).

## Warning: Removed 16 rows containing missing values (geom_point).

## Warning: Removed 16 rows containing missing values (geom_point).

##                       cor.y exclude.as.feat  cor.y.abs    cor.high.X
## resX.max.nexp    0.03154383               0 0.03154383          <NA>
## resY.max.nexp    0.01909048               0 0.01909048          <NA>
## resY.max         0.01165671               0 0.01165671          <NA>
## resY.max.root2   0.01155620               0 0.01155620      resY.max
## resY.max.log1p   0.01145137               0 0.01145137      resY.max
## resX.max        -0.03154383               0 0.03154383 resX.max.nexp
## resX.max.log1p  -0.03154383               0 0.03154383      resX.max
## resX.max.root2  -0.03154383               0 0.03154383 resX.max.nexp
## resXY.max.nexp           NA               0         NA          <NA>
## resXY.mean.nexp          NA               0         NA          <NA>
## resXY.min.nexp           NA               0         NA          <NA>
##                 freqRatio percentUnique zeroVar  nzv is.cor.y.abs.low
## resX.max.nexp         999          0.10   FALSE TRUE            FALSE
## resY.max.nexp        1997          0.20   FALSE TRUE            FALSE
## resY.max             1997          0.20   FALSE TRUE            FALSE
## resY.max.root2       1997          0.20   FALSE TRUE            FALSE
## resY.max.log1p       1997          0.20   FALSE TRUE            FALSE
## resX.max              999          0.10   FALSE TRUE            FALSE
## resX.max.log1p        999          0.10   FALSE TRUE            FALSE
## resX.max.root2        999          0.10   FALSE TRUE            FALSE
## resXY.max.nexp          0          0.05    TRUE TRUE               NA
## resXY.mean.nexp         0          0.05    TRUE TRUE               NA
## resXY.min.nexp          0          0.05    TRUE TRUE               NA

## [1] "numeric data missing in glbObsAll: "
##        lunch       dinner      reserve      outdoor    expensive 
##        10000        10000        10000        10000        10000 
##       liquor        table       classy         kids outdoor.fctr 
##        10000        10000        10000        10000        10000 
## [1] "numeric data w/ 0s in glbObsAll: "
##      nImgs.nexp        resX.mad  resX.mad.log1p  resX.mad.root2 
##             228            9353            9353            9353 
##        resY.mad  resY.mad.log1p  resY.mad.root2       resXY.mad 
##            5442            5442            5442           10915 
##  resXY.min.nexp  resXY.max.nexp resXY.mean.nexp resXY.mad.log1p 
##           12000           12000           12000           10915 
## resXY.mad.root2  resXY.mad.nexp           lunch 
##           10915             850             671 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## business_id      labels     resXLst     resYLst        .lcn 
##           0          NA           0           0       10000
## [1] "glb_feats_df:"
## [1] 64 12
##                        id exclude.as.feat rsp_var
## outdoor.fctr outdoor.fctr            TRUE    TRUE
##                        id cor.y exclude.as.feat cor.y.abs cor.high.X
## outdoor           outdoor     1            TRUE         1       <NA>
## outdoor.fctr outdoor.fctr    NA            TRUE        NA       <NA>
##              freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## outdoor       1.006018           0.1   FALSE FALSE            FALSE
## outdoor.fctr        NA            NA      NA    NA               NA
##              interaction.feat shapiro.test.p.value rsp_var_raw id_var
## outdoor                    NA                   NA        TRUE     NA
## outdoor.fctr               NA                   NA          NA     NA
##              rsp_var
## outdoor           NA
## outdoor.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn     end
## 15 select.features          7          0           0 129.924 132.968
## 16      fit.models          8          0           0 132.969      NA
##    elapsed
## 15   3.045
## 16      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 133.588  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
c("id.prefix", "method", "type",
  # trainControl params
  "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
  # train params
  "metric", "metric.maximize", "tune.df")
##  [1] "id.prefix"       "method"          "type"           
##  [4] "preProc.method"  "cv.n.folds"      "cv.n.repeats"   
##  [7] "summary.fn"      "metric"          "metric.maximize"
## [10] "tune.df"
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indep_vars_vctr=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indep_vars = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indep_vars = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 133.588 133.622
## 2 fit.models_0_MFO          1          1 myMFO_classfr 133.623      NA
##   elapsed
## 1   0.034
## 2      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indep_vars: .rnorm"
## [1] "myfit_mdl: setup complete: 0.412000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
##        Y        N 
## 0.500998 0.499002 
## [1] "MFO.val:"
## [1] "Y"
## [1] "myfit_mdl: train complete: 0.841000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.843000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##          N        Y
## 1 0.500998 0.499002
## 2 0.500998 0.499002
## 3 0.500998 0.499002
## 4 0.500998 0.499002
## 5 0.500998 0.499002
## 6 0.500998 0.499002

##          Prediction
## Reference   N   Y
##         N   0 500
##         Y   0 502
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.009980e-01   0.000000e+00   4.695761e-01   5.324140e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   5.126083e-01  2.586405e-110 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##          N        Y
## 1 0.500998 0.499002
## 2 0.500998 0.499002
## 3 0.500998 0.499002
## 4 0.500998 0.499002
## 5 0.500998 0.499002
## 6 0.500998 0.499002

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 3.129000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.422
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.002             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.4       0.6675532         0.500998
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4695761              0.532414             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.4       0.6684456         0.502004
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4705156             0.5334806             0
## [1] "myfit_mdl: exit: 3.139000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 133.623
## 3 fit.models_0_Random          1          2 myrandom_classfr 136.767
##       end elapsed
## 2 136.767   3.144
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] "    indep_vars: .rnorm"
## [1] "myfit_mdl: setup complete: 0.427000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.774000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.775000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   N   Y
##         N   0 500
##         Y   0 502
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.009980e-01   0.000000e+00   4.695761e-01   5.324140e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   5.126083e-01  2.586405e-110 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 3.343000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.343                 0.002       0.4980239
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1        0.508    0.4880478        0.487012                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6675532         0.500998             0.4695761
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1              0.532414             0       0.5059679    0.4949698
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5169661       0.4969618                    0.4       0.6684456
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1         0.502004             0.4705156             0.5334806
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 3.356000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 136.767 140.136   3.369
## 4 140.137      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
    id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
    train.method="glmnet")),
                    indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indep_vars: nImgs.cut.fctr,resY.min"
## [1] "myfit_mdl: setup complete: 0.674000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-2
## Fitting alpha = 0.1, lambda = 0.000488 on full training set
## [1] "myfit_mdl: train complete: 1.460000 secs"

##             Length Class      Mode     
## a0           41    -none-     numeric  
## beta        164    dgCMatrix  S4       
## df           41    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       41    -none-     numeric  
## dev.ratio    41    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)     nImgs.cut.fctr(32,60] 
##                -0.2246350                 0.2599168 
##    nImgs.cut.fctr(60,120] nImgs.cut.fctr(120,3e+03] 
##                 0.2554564                 0.3976048 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"               "nImgs.cut.fctr(32,60]"    
## [3] "nImgs.cut.fctr(60,120]"    "nImgs.cut.fctr(120,3e+03]"
## [5] "resY.min"                 
## [1] "myfit_mdl: train diagnostics complete: 1.580000 secs"

##          Prediction
## Reference   N   Y
##         N   0 500
##         Y   0 502
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.009980e-01   0.000000e+00   4.695761e-01   5.324140e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   5.126083e-01  2.586405e-110

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 3.557000 secs"
##                           id                   feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet nImgs.cut.fctr,resY.min               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       0.78                 0.017       0.5304143
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1        0.268    0.7928287        0.539247                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6675532         0.500998             0.4695761
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1              0.532414             0       0.5300104    0.2676056
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.7924152       0.5290506                    0.4       0.6684456
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1         0.502004             0.4705156             0.5334806
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 3.570000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indep_vars: nImgs.cut.fctr,resY.min"
## [1] "myfit_mdl: setup complete: 0.669000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.012 on full training set
## [1] "myfit_mdl: train complete: 2.365000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1002 
## 
##      CP nsplit rel error
## 1 0.070      0     1.000
## 2 0.044      1     0.930
## 3 0.017      2     0.886
## 4 0.014      4     0.852
## 5 0.012      5     0.838
## 
## Variable importance
##                  resY.min    nImgs.cut.fctr(60,120] 
##                        89                         7 
## nImgs.cut.fctr(120,3e+03]     nImgs.cut.fctr(32,60] 
##                         3                         1 
## 
## Node number 1: 1002 observations,    complexity param=0.07
##   predicted class=Y  expected loss=0.499002  P(node) =1
##     class counts:   500   502
##    probabilities: 0.499 0.501 
##   left son=2 (509 obs) right son=3 (493 obs)
##   Primary splits:
##       resY.min                  < 261.5 to the right, improve=2.58978200, (0 missing)
##       nImgs.cut.fctr(120,3e+03] < 0.5   to the left,  improve=1.19439200, (0 missing)
##       nImgs.cut.fctr(32,60]     < 0.5   to the left,  improve=0.07380192, (0 missing)
##       nImgs.cut.fctr(60,120]    < 0.5   to the left,  improve=0.05268239, (0 missing)
##   Surrogate splits:
##       nImgs.cut.fctr(120,3e+03] < 0.5   to the left,  agree=0.589, adj=0.164, (0 split)
##       nImgs.cut.fctr(32,60]     < 0.5   to the right, agree=0.538, adj=0.061, (0 split)
##       nImgs.cut.fctr(60,120]    < 0.5   to the left,  agree=0.525, adj=0.034, (0 split)
## 
## Node number 2: 509 observations,    complexity param=0.017
##   predicted class=N  expected loss=0.4656189  P(node) =0.507984
##     class counts:   272   237
##    probabilities: 0.534 0.466 
##   left son=4 (389 obs) right son=5 (120 obs)
##   Primary splits:
##       nImgs.cut.fctr(60,120]    < 0.5   to the left,  improve=1.107328000, (0 missing)
##       resY.min                  < 372.5 to the right, improve=1.096090000, (0 missing)
##       nImgs.cut.fctr(32,60]     < 0.5   to the left,  improve=0.378170100, (0 missing)
##       nImgs.cut.fctr(120,3e+03] < 0.5   to the left,  improve=0.009365927, (0 missing)
## 
## Node number 3: 493 observations,    complexity param=0.044
##   predicted class=Y  expected loss=0.4624746  P(node) =0.492016
##     class counts:   228   265
##    probabilities: 0.462 0.538 
##   left son=6 (130 obs) right son=7 (363 obs)
##   Primary splits:
##       resY.min                  < 136   to the left,  improve=5.26786600, (0 missing)
##       nImgs.cut.fctr(120,3e+03] < 0.5   to the left,  improve=0.84070860, (0 missing)
##       nImgs.cut.fctr(60,120]    < 0.5   to the right, improve=0.64330140, (0 missing)
##       nImgs.cut.fctr(32,60]     < 0.5   to the right, improve=0.00280855, (0 missing)
## 
## Node number 4: 389 observations
##   predicted class=N  expected loss=0.4473008  P(node) =0.3882236
##     class counts:   215   174
##    probabilities: 0.553 0.447 
## 
## Node number 5: 120 observations,    complexity param=0.017
##   predicted class=Y  expected loss=0.475  P(node) =0.1197605
##     class counts:    57    63
##    probabilities: 0.475 0.525 
##   left son=10 (23 obs) right son=11 (97 obs)
##   Primary splits:
##       resY.min < 280.5 to the left,  improve=3.970126, (0 missing)
## 
## Node number 6: 130 observations,    complexity param=0.014
##   predicted class=N  expected loss=0.4153846  P(node) =0.1297405
##     class counts:    76    54
##    probabilities: 0.585 0.415 
##   left son=12 (93 obs) right son=13 (37 obs)
##   Primary splits:
##       resY.min                  < 94.5  to the right, improve=3.32212900, (0 missing)
##       nImgs.cut.fctr(32,60]     < 0.5   to the right, improve=0.13810200, (0 missing)
##       nImgs.cut.fctr(60,120]    < 0.5   to the right, improve=0.06990362, (0 missing)
##       nImgs.cut.fctr(120,3e+03] < 0.5   to the left,  improve=0.02157842, (0 missing)
## 
## Node number 7: 363 observations
##   predicted class=Y  expected loss=0.4187328  P(node) =0.3622754
##     class counts:   152   211
##    probabilities: 0.419 0.581 
## 
## Node number 10: 23 observations
##   predicted class=N  expected loss=0.2608696  P(node) =0.02295409
##     class counts:    17     6
##    probabilities: 0.739 0.261 
## 
## Node number 11: 97 observations
##   predicted class=Y  expected loss=0.4123711  P(node) =0.09680639
##     class counts:    40    57
##    probabilities: 0.412 0.588 
## 
## Node number 12: 93 observations
##   predicted class=N  expected loss=0.344086  P(node) =0.09281437
##     class counts:    61    32
##    probabilities: 0.656 0.344 
## 
## Node number 13: 37 observations
##   predicted class=Y  expected loss=0.4054054  P(node) =0.03692615
##     class counts:    15    22
##    probabilities: 0.405 0.595 
## 
## n= 1002 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 1002 500 Y (0.4990020 0.5009980)  
##    2) resY.min>=261.5 509 237 N (0.5343811 0.4656189)  
##      4) nImgs.cut.fctr(60,120]< 0.5 389 174 N (0.5526992 0.4473008) *
##      5) nImgs.cut.fctr(60,120]>=0.5 120  57 Y (0.4750000 0.5250000)  
##       10) resY.min< 280.5 23   6 N (0.7391304 0.2608696) *
##       11) resY.min>=280.5 97  40 Y (0.4123711 0.5876289) *
##    3) resY.min< 261.5 493 228 Y (0.4624746 0.5375254)  
##      6) resY.min< 136 130  54 N (0.5846154 0.4153846)  
##       12) resY.min>=94.5 93  32 N (0.6559140 0.3440860) *
##       13) resY.min< 94.5 37  15 Y (0.4054054 0.5945946) *
##      7) resY.min>=136 363 152 Y (0.4187328 0.5812672) *
## [1] "myfit_mdl: train diagnostics complete: 3.404000 secs"

##          Prediction
## Reference   N   Y
##         N  17 483
##         Y   6 496
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.119760e-01   2.208979e-02   4.805306e-01   5.433509e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   2.535456e-01  8.991577e-103

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 5.515000 secs"
##                     id                   feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart nImgs.cut.fctr,resY.min               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       1.69                 0.016       0.5818446
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1        0.586    0.5776892       0.5938127                    0.3
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6698177        0.5255967             0.4805306
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5433509    0.05146235       0.5039318    0.4849095
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5229541       0.4985602                    0.2       0.6684456
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1         0.502004             0.4705156             0.5334806
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0         0.02756072      0.05485113
## [1] "myfit_mdl: exit: 5.530000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 140.137 149.278   9.142
## 5 149.279      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indep_vars: nImgs.cut.fctr,resY.min,nImgs.cut.fctr:nImgs.cut.fctr,nImgs.cut.fctr:resX.mad.log1p,nImgs.cut.fctr:nImgs.log1p,nImgs.cut.fctr:resY.mean.log1p,nImgs.cut.fctr:resY.mean.root2,nImgs.cut.fctr:resXY.mad,nImgs.cut.fctr:resXY.mad.nexp,nImgs.cut.fctr:resX.mean,nImgs.cut.fctr:resX.mean.nexp,nImgs.cut.fctr:resX.min,nImgs.cut.fctr:resXY.min,nImgs.cut.fctr:resY.min"
## [1] "myfit_mdl: setup complete: 0.683000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 4.65e-05 on full training set
## [1] "myfit_mdl: train complete: 23.119000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0            69   -none-     numeric  
## beta        3243   dgCMatrix  S4       
## df            69   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        69   -none-     numeric  
## dev.ratio     69   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        47   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                               (Intercept) 
##                              6.115004e+00 
##                     nImgs.cut.fctr(32,60] 
##                             -1.088896e+01 
##                 nImgs.cut.fctr(120,3e+03] 
##                              9.149823e-01 
##                                  resY.min 
##                             -1.058372e-04 
##          nImgs.cut.fctr(0,32]:nImgs.log1p 
##                              7.957820e-01 
##         nImgs.cut.fctr(32,60]:nImgs.log1p 
##                              1.712224e-01 
##        nImgs.cut.fctr(60,120]:nImgs.log1p 
##                             -4.345255e-01 
##     nImgs.cut.fctr(120,3e+03]:nImgs.log1p 
##                             -1.625661e-01 
##       nImgs.cut.fctr(0,32]:resX.mad.log1p 
##                              1.293689e-02 
##      nImgs.cut.fctr(32,60]:resX.mad.log1p 
##                              3.942754e-02 
##  nImgs.cut.fctr(120,3e+03]:resX.mad.log1p 
##                              1.448236e-01 
##            nImgs.cut.fctr(0,32]:resX.mean 
##                             -7.688121e-03 
##           nImgs.cut.fctr(32,60]:resX.mean 
##                              9.164700e-03 
##          nImgs.cut.fctr(60,120]:resX.mean 
##                             -2.526216e-03 
##       nImgs.cut.fctr(120,3e+03]:resX.mean 
##                             -2.297982e-02 
##       nImgs.cut.fctr(0,32]:resX.mean.nexp 
##                              9.900000e+35 
##      nImgs.cut.fctr(32,60]:resX.mean.nexp 
##                              9.900000e+35 
##             nImgs.cut.fctr(0,32]:resX.min 
##                              3.544555e-03 
##            nImgs.cut.fctr(32,60]:resX.min 
##                              3.062510e-03 
##           nImgs.cut.fctr(60,120]:resX.min 
##                             -4.431001e-03 
##        nImgs.cut.fctr(120,3e+03]:resX.min 
##                              1.283089e-02 
##            nImgs.cut.fctr(0,32]:resXY.mad 
##                              1.366340e-05 
##           nImgs.cut.fctr(32,60]:resXY.mad 
##                             -2.024373e-06 
##          nImgs.cut.fctr(60,120]:resXY.mad 
##                              1.076650e-05 
##       nImgs.cut.fctr(120,3e+03]:resXY.mad 
##                              2.648629e-05 
##       nImgs.cut.fctr(0,32]:resXY.mad.nexp 
##                              7.092943e-02 
##      nImgs.cut.fctr(32,60]:resXY.mad.nexp 
##                              3.051364e-01 
##     nImgs.cut.fctr(60,120]:resXY.mad.nexp 
##                              2.404053e-01 
##  nImgs.cut.fctr(120,3e+03]:resXY.mad.nexp 
##                              6.629458e-01 
##            nImgs.cut.fctr(0,32]:resXY.min 
##                             -4.192213e-06 
##           nImgs.cut.fctr(32,60]:resXY.min 
##                             -1.379372e-05 
##          nImgs.cut.fctr(60,120]:resXY.min 
##                              1.487104e-05 
##       nImgs.cut.fctr(120,3e+03]:resXY.min 
##                             -1.299658e-05 
##     nImgs.cut.fctr(32,60]:resY.mean.log1p 
##                             -2.320909e-01 
## nImgs.cut.fctr(120,3e+03]:resY.mean.log1p 
##                              1.325613e-01 
##      nImgs.cut.fctr(0,32]:resY.mean.root2 
##                             -2.987543e-01 
##     nImgs.cut.fctr(32,60]:resY.mean.root2 
##                              8.509761e-03 
##    nImgs.cut.fctr(60,120]:resY.mean.root2 
##                             -1.253344e-01 
##            nImgs.cut.fctr(32,60]:resY.min 
##                              7.070070e-03 
##           nImgs.cut.fctr(60,120]:resY.min 
##                             -4.026836e-03 
##        nImgs.cut.fctr(120,3e+03]:resY.min 
##                              2.738971e-03 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"                              
##  [2] "nImgs.cut.fctr(32,60]"                    
##  [3] "nImgs.cut.fctr(60,120]"                   
##  [4] "nImgs.cut.fctr(120,3e+03]"                
##  [5] "resY.min"                                 
##  [6] "nImgs.cut.fctr(0,32]:nImgs.log1p"         
##  [7] "nImgs.cut.fctr(32,60]:nImgs.log1p"        
##  [8] "nImgs.cut.fctr(60,120]:nImgs.log1p"       
##  [9] "nImgs.cut.fctr(120,3e+03]:nImgs.log1p"    
## [10] "nImgs.cut.fctr(0,32]:resX.mad.log1p"      
## [11] "nImgs.cut.fctr(32,60]:resX.mad.log1p"     
## [12] "nImgs.cut.fctr(60,120]:resX.mad.log1p"    
## [13] "nImgs.cut.fctr(120,3e+03]:resX.mad.log1p" 
## [14] "nImgs.cut.fctr(0,32]:resX.mean"           
## [15] "nImgs.cut.fctr(32,60]:resX.mean"          
## [16] "nImgs.cut.fctr(60,120]:resX.mean"         
## [17] "nImgs.cut.fctr(120,3e+03]:resX.mean"      
## [18] "nImgs.cut.fctr(0,32]:resX.mean.nexp"      
## [19] "nImgs.cut.fctr(32,60]:resX.mean.nexp"     
## [20] "nImgs.cut.fctr(60,120]:resX.mean.nexp"    
## [21] "nImgs.cut.fctr(120,3e+03]:resX.mean.nexp" 
## [22] "nImgs.cut.fctr(0,32]:resX.min"            
## [23] "nImgs.cut.fctr(32,60]:resX.min"           
## [24] "nImgs.cut.fctr(60,120]:resX.min"          
## [25] "nImgs.cut.fctr(120,3e+03]:resX.min"       
## [26] "nImgs.cut.fctr(0,32]:resXY.mad"           
## [27] "nImgs.cut.fctr(32,60]:resXY.mad"          
## [28] "nImgs.cut.fctr(60,120]:resXY.mad"         
## [29] "nImgs.cut.fctr(120,3e+03]:resXY.mad"      
## [30] "nImgs.cut.fctr(0,32]:resXY.mad.nexp"      
## [31] "nImgs.cut.fctr(32,60]:resXY.mad.nexp"     
## [32] "nImgs.cut.fctr(60,120]:resXY.mad.nexp"    
## [33] "nImgs.cut.fctr(120,3e+03]:resXY.mad.nexp" 
## [34] "nImgs.cut.fctr(0,32]:resXY.min"           
## [35] "nImgs.cut.fctr(32,60]:resXY.min"          
## [36] "nImgs.cut.fctr(60,120]:resXY.min"         
## [37] "nImgs.cut.fctr(120,3e+03]:resXY.min"      
## [38] "nImgs.cut.fctr(0,32]:resY.mean.log1p"     
## [39] "nImgs.cut.fctr(32,60]:resY.mean.log1p"    
## [40] "nImgs.cut.fctr(60,120]:resY.mean.log1p"   
## [41] "nImgs.cut.fctr(120,3e+03]:resY.mean.log1p"
## [42] "nImgs.cut.fctr(0,32]:resY.mean.root2"     
## [43] "nImgs.cut.fctr(32,60]:resY.mean.root2"    
## [44] "nImgs.cut.fctr(60,120]:resY.mean.root2"   
## [45] "nImgs.cut.fctr(120,3e+03]:resY.mean.root2"
## [46] "nImgs.cut.fctr(32,60]:resY.min"           
## [47] "nImgs.cut.fctr(60,120]:resY.min"          
## [48] "nImgs.cut.fctr(120,3e+03]:resY.min"       
## [1] "myfit_mdl: train diagnostics complete: 23.726000 secs"

##          Prediction
## Reference   N   Y
##         N  25 475
##         Y   8 494
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.179641e-01   3.412724e-02   4.865121e-01   5.493102e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   1.485851e-01  8.821190e-100

##          Prediction
## Reference   N   Y
##         N   7 490
##         Y   2 499
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.070140e-01   1.013221e-02   4.755136e-01   5.384730e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   3.878866e-01  7.647039e-107 
## [1] "myfit_mdl: predict complete: 26.607000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                 feats
## 1 nImgs.cut.fctr,resY.min,nImgs.cut.fctr:nImgs.cut.fctr,nImgs.cut.fctr:resX.mad.log1p,nImgs.cut.fctr:nImgs.log1p,nImgs.cut.fctr:resY.mean.log1p,nImgs.cut.fctr:resY.mean.root2,nImgs.cut.fctr:resXY.mad,nImgs.cut.fctr:resXY.mad.nexp,nImgs.cut.fctr:resX.mean,nImgs.cut.fctr:resX.mean.nexp,nImgs.cut.fctr:resX.min,nImgs.cut.fctr:resXY.min,nImgs.cut.fctr:resY.min
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     22.427                 1.069
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6058048        0.614    0.5976096        0.642749
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.3       0.6716519        0.5448976
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4865121             0.5493102    0.08975773
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5040041    0.5030181      0.50499       0.5140745
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.2       0.6697987         0.507014
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4755136              0.538473    0.01013221
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.02621502      0.05242679
## [1] "myfit_mdl: exit: 26.621000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn     end elapsed
## 5 149.279 175.936  26.657
## 6 175.936      NA      NA
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv & 
                              (exclude.as.feat != 1))[, "id"]  
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indep_vars = indep_vars, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indep_vars: nImgs.cut.fctr,.pos,resX.mad.log1p,resXY.mad.nexp,resY.mean.log1p,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad,nImgs,resX.mean,resX.min.nexp,resX.mean.nexp,resY.min.nexp,resX.min,resY.min"
## [1] "myfit_mdl: setup complete: 0.674000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 2.27e-05 on full training set
## [1] "myfit_mdl: train complete: 7.163000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        2700   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        27   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                      .pos 
##             -5.137400e+02              9.469314e-05 
##                    .rnorm                     nImgs 
##             -6.696257e-02             -6.025466e-04 
##     nImgs.cut.fctr(32,60]    nImgs.cut.fctr(60,120] 
##              3.055406e-01              3.605103e-01 
## nImgs.cut.fctr(120,3e+03]                nImgs.nexp 
##              7.032784e-01              3.883642e+01 
##            resX.mad.log1p                 resX.mean 
##              2.946340e-02              1.789300e-02 
##            resX.mean.nexp                  resX.min 
##              9.900000e+35              1.896118e-03 
##             resX.min.nexp                 resXY.mad 
##             -2.074840e+27             -6.363773e-08 
##            resXY.mad.nexp                 resXY.max 
##              2.331664e-01             -1.796879e-04 
##           resXY.max.log1p           resXY.max.root2 
##              3.256226e+01              3.115691e-02 
##                resXY.mean          resXY.mean.log1p 
##             -1.086317e-04              8.228750e+00 
##          resXY.mean.root2                  resY.mad 
##             -4.986359e-03              1.063081e-02 
##            resY.mad.log1p             resY.mad.nexp 
##              1.163426e+00              1.078418e+00 
##            resY.mad.root2           resY.mean.log1p 
##             -5.324178e-01              8.403533e+00 
##                  resY.min             resY.min.nexp 
##             -8.449056e-04             -7.675900e+21 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"               ".pos"                     
##  [3] ".rnorm"                    "nImgs"                    
##  [5] "nImgs.cut.fctr(32,60]"     "nImgs.cut.fctr(60,120]"   
##  [7] "nImgs.cut.fctr(120,3e+03]" "nImgs.nexp"               
##  [9] "resX.mad.log1p"            "resX.mean"                
## [11] "resX.mean.nexp"            "resX.min"                 
## [13] "resX.min.nexp"             "resXY.mad"                
## [15] "resXY.mad.nexp"            "resXY.max"                
## [17] "resXY.max.log1p"           "resXY.max.root2"          
## [19] "resXY.mean"                "resXY.mean.log1p"         
## [21] "resXY.mean.root2"          "resY.mad"                 
## [23] "resY.mad.log1p"            "resY.mad.nexp"            
## [25] "resY.mad.root2"            "resY.mean.log1p"          
## [27] "resY.min"                  "resY.min.nexp"            
## [1] "myfit_mdl: train diagnostics complete: 7.771000 secs"

##          Prediction
## Reference   N   Y
##         N  13 487
##         Y   1 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.129741e-01   2.405454e-02   4.815272e-01   5.443444e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   2.337476e-01  7.772075e-107

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 10.810000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                    feats
## 1 nImgs.cut.fctr,.pos,resX.mad.log1p,resXY.mad.nexp,resY.mean.log1p,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad,nImgs,resX.mean,resX.min.nexp,resX.mean.nexp,resY.min.nexp,resX.min,resY.min
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      6.475                 0.633
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5638247         0.54    0.5876494       0.5929761
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.3       0.6724832        0.5265996
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4815272             0.5443444    0.05311189
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.4978875    0.4708249    0.5249501       0.5012952
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                      0       0.6684456         0.502004
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4705156             0.5334806             0
##   max.AccuracySD.fit max.KappaSD.fit
## 1          0.0195775      0.03917575
## [1] "myfit_mdl: exit: 10.825000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn    end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 175.936 186.81
## 7       fit.models_0_end          1          6    teardown 186.811     NA
##   elapsed
## 6  10.874
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          8          0           0 132.969 186.823  53.854
## 17 fit.models          8          1           1 186.824      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 190.945  NA      NA
# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indep_vars <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjust_interaction_feats(myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
            indep_vars <- setdiff(indep_vars, topindep_var)
            if (length(interact_vars) > 0) {
                indep_vars <- 
                    setdiff(indep_vars, myextract_actual_feats(interact_vars))
                indep_vars <- c(indep_vars, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indep_vars <- union(indep_vars, topindep_var)
        }
    }
    
    if (is.null(indep_vars))
        indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
        indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indep_vars))
        indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
    
    if ((length(indep_vars) == 1) && (grepl("^%<d-%", indep_vars))) {    
        indep_vars <- 
            eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
    }    

    indep_vars <- myadjust_interaction_feats(indep_vars)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indep_vars <- setdiff(indep_vars, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indep_vars = indep_vars, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indep_vars]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indep_vars]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 190.945 190.955
## 2 fit.models_1_All.X          1          1       setup 190.956      NA
##   elapsed
## 1    0.01
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 190.956 190.963
## 3 fit.models_1_All.X          1          2      glmnet 190.964      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indep_vars: nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min"
## [1] "myfit_mdl: setup complete: 0.710000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 2.45e-05 on full training set
## [1] "myfit_mdl: train complete: 11.771000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        4600   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        46   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                      .pos 
##             -5.322001e+02              1.037459e-04 
##                    .rnorm                     nImgs 
##             -6.769060e-02              1.521544e-04 
##     nImgs.cut.fctr(32,60]    nImgs.cut.fctr(60,120] 
##              1.309604e-01              6.523010e-02 
## nImgs.cut.fctr(120,3e+03]               nImgs.log1p 
##              3.460511e-01              5.930509e-01 
##                nImgs.nexp               nImgs.root2 
##              5.294506e+01             -1.033055e-01 
##                  resX.mad            resX.mad.log1p 
##             -7.462293e-03              3.347949e-01 
##             resX.mad.nexp            resX.mad.root2 
##              8.597647e-01              1.847927e-02 
##                 resX.mean           resX.mean.log1p 
##             -2.908604e-02              1.718262e+01 
##            resX.mean.nexp           resX.mean.root2 
##              9.900000e+35             -1.044154e-01 
##                  resX.min            resX.min.log1p 
##              1.111873e-02             -9.111545e+00 
##             resX.min.nexp            resX.min.root2 
##             -3.246667e+27              7.845843e-01 
##                 resXY.mad           resXY.mad.log1p 
##              2.701393e-06              1.155758e-01 
##            resXY.mad.nexp           resXY.mad.root2 
##              1.030157e+00             -2.916969e-03 
##                 resXY.max           resXY.max.log1p 
##             -1.265518e-04              3.573686e+01 
##           resXY.max.root2                resXY.mean 
##             -3.382568e-02             -1.511560e-04 
##          resXY.mean.log1p          resXY.mean.root2 
##              1.984867e+01             -6.256731e-03 
##                 resXY.min           resXY.min.log1p 
##             -3.496593e-05              1.785193e+00 
##           resXY.min.root2                  resY.mad 
##              8.746218e-03              1.065394e-02 
##            resY.mad.log1p             resY.mad.nexp 
##              1.160382e+00              1.082350e+00 
##            resY.mad.root2                 resY.mean 
##             -5.316544e-01              7.943836e-02 
##           resY.mean.log1p            resY.mean.nexp 
##             -2.938950e+01              9.900000e+35 
##           resY.mean.root2                  resY.min 
##              1.030505e-01              9.792523e-04 
##            resY.min.log1p             resY.min.nexp 
##             -2.872098e+00             -6.257821e+21 
##            resY.min.root2 
##              3.282024e-01 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"               ".pos"                     
##  [3] ".rnorm"                    "nImgs"                    
##  [5] "nImgs.cut.fctr(32,60]"     "nImgs.cut.fctr(60,120]"   
##  [7] "nImgs.cut.fctr(120,3e+03]" "nImgs.log1p"              
##  [9] "nImgs.nexp"                "nImgs.root2"              
## [11] "resX.mad"                  "resX.mad.log1p"           
## [13] "resX.mad.nexp"             "resX.mad.root2"           
## [15] "resX.mean"                 "resX.mean.log1p"          
## [17] "resX.mean.nexp"            "resX.mean.root2"          
## [19] "resX.min"                  "resX.min.log1p"           
## [21] "resX.min.nexp"             "resX.min.root2"           
## [23] "resXY.mad"                 "resXY.mad.log1p"          
## [25] "resXY.mad.nexp"            "resXY.mad.root2"          
## [27] "resXY.max"                 "resXY.max.log1p"          
## [29] "resXY.max.root2"           "resXY.mean"               
## [31] "resXY.mean.log1p"          "resXY.mean.root2"         
## [33] "resXY.min"                 "resXY.min.log1p"          
## [35] "resXY.min.root2"           "resY.mad"                 
## [37] "resY.mad.log1p"            "resY.mad.nexp"            
## [39] "resY.mad.root2"            "resY.mean"                
## [41] "resY.mean.log1p"           "resY.mean.nexp"           
## [43] "resY.mean.root2"           "resY.min"                 
## [45] "resY.min.log1p"            "resY.min.nexp"            
## [47] "resY.min.root2"           
## [1] "myfit_mdl: train diagnostics complete: 12.473000 secs"

##          Prediction
## Reference   N   Y
##         N  32 468
##         Y   4 498
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.289421e-01   5.613565e-02   4.974896e-01   5.602241e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   4.111828e-02  8.917990e-101

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 16.090000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 feats
## 1 nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     11.043                 1.193
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5678247        0.548    0.5876494       0.6177371
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.3       0.6784741        0.5299353
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4974896             0.5602241    0.05971262
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5108937    0.4788732    0.5429142       0.5204922
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                      0       0.6684456         0.502004
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4705156             0.5334806             0
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.02333108      0.04665017
## [1] "myfit_mdl: exit: 16.105000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 190.964 207.079
## 4 fit.models_1_All.X          1          3         glm 207.080      NA
##   elapsed
## 3  16.115
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indep_vars: nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min"
## [1] "myfit_mdl: setup complete: 0.689000 secs"
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.195000 secs"

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.026  -1.143   0.266   1.125   1.788  
## 
## Coefficients:
##                                Estimate  Std. Error z value Pr(>|z|)  
## (Intercept)                   2.708e+04   2.491e+04   1.087   0.2768  
## .pos                          1.096e-04   1.152e-04   0.951   0.3414  
## .rnorm                       -7.722e-02   6.399e-02  -1.207   0.2275  
## nImgs                        -6.547e-05   2.101e-03  -0.031   0.9751  
## `nImgs.cut.fctr(32,60]`       1.116e-01   3.067e-01   0.364   0.7160  
## `nImgs.cut.fctr(60,120]`      3.871e-02   4.372e-01   0.089   0.9294  
## `nImgs.cut.fctr(120,3e+03]`   3.151e-01   6.030e-01   0.523   0.6012  
## nImgs.log1p                   5.186e-01   6.587e-01   0.787   0.4311  
## nImgs.nexp                   -2.328e+00   3.413e+02  -0.007   0.9946  
## nImgs.root2                  -8.601e-02   1.606e-01  -0.535   0.5924  
## resX.mad                     -2.205e-02   4.616e-02  -0.478   0.6329  
## resX.mad.log1p               -1.827e-01   1.841e+00  -0.099   0.9210  
## resX.mad.nexp                 8.272e-01   8.112e-01   1.020   0.3079  
## resX.mad.root2                4.028e-01   1.259e+00   0.320   0.7491  
## resX.mean                     9.477e-01   8.155e+00   0.116   0.9075  
## resX.mean.log1p               4.437e+02   3.494e+03   0.127   0.8989  
## resX.mean.nexp               4.704e+153         Inf   0.000   1.0000  
## resX.mean.root2              -8.156e+01   6.745e+02  -0.121   0.9038  
## resX.min                     -1.323e-01   9.231e-02  -1.433   0.1518  
## resX.min.log1p               -4.177e+01   2.048e+01  -2.039   0.0414 *
## resX.min.nexp                -4.471e+27   1.783e+28  -0.251   0.8020  
## resX.min.root2                9.501e+00   5.524e+00   1.720   0.0855 .
## resXY.mad                     4.489e-06   9.404e-05   0.048   0.9619  
## resXY.mad.log1p               1.905e-01   1.115e+00   0.171   0.8644  
## resXY.mad.nexp                1.496e+00   6.532e+00   0.229   0.8189  
## resXY.mad.root2              -4.913e-03   4.524e-02  -0.109   0.9135  
## resXY.max                    -4.066e-03   8.854e-03  -0.459   0.6461  
## resXY.max.log1p              -7.553e+02   1.902e+03  -0.397   0.6912  
## resXY.max.root2               7.039e+00   1.643e+01   0.429   0.6683  
## resXY.mean                   -1.351e-02   1.013e-02  -1.333   0.1825  
## resXY.mean.log1p             -2.276e+03   1.758e+03  -1.295   0.1954  
## resXY.mean.root2              2.216e+01   1.689e+01   1.312   0.1894  
## resXY.min                     2.897e-05   5.299e-05   0.547   0.5846  
## resXY.min.log1p               5.862e+00   3.358e+00   1.746   0.0808 .
## resXY.min.root2              -5.903e-02   5.392e-02  -1.095   0.2736  
## resY.mad                      5.300e-02   3.672e-02   1.443   0.1489  
## resY.mad.log1p                2.907e+00   1.560e+00   1.863   0.0624 .
## resY.mad.nexp                 1.627e+00   7.684e-01   2.118   0.0342 *
## resY.mad.root2               -1.715e+00   1.035e+00  -1.657   0.0975 .
## resY.mean                    -4.775e-01   3.396e+00  -0.141   0.8882  
## resY.mean.log1p              -2.850e+02   1.385e+03  -0.206   0.8370  
## resY.mean.nexp              -2.233e+129  3.157e+130  -0.071   0.9436  
## resY.mean.root2               4.797e+01   2.739e+02   0.175   0.8610  
## resY.min                     -6.743e-02   6.238e-02  -1.081   0.2798  
## resY.min.log1p               -1.617e+01   1.197e+01  -1.350   0.1769  
## resY.min.nexp                -8.983e+21   1.379e+22  -0.651   0.5147  
## resY.min.root2                4.196e+00   3.476e+00   1.207   0.2274  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1389.1  on 1001  degrees of freedom
## Residual deviance: 1326.1  on  955  degrees of freedom
## AIC: 1420.1
## 
## Number of Fisher Scoring iterations: 11
## 
## [1] "myfit_mdl: train diagnostics complete: 3.024000 secs"

##          Prediction
## Reference   N   Y
##         N  43 457
##         Y  10 492
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.339321e-01   6.619757e-02   5.024844e-01   5.651800e-01   5.009980e-01 
## AccuracyPValue  McnemarPValue 
##   1.998543e-02   1.240552e-94

##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.020040e-01   0.000000e+00   4.705156e-01   5.334806e-01   5.020040e-01 
## AccuracyPValue  McnemarPValue 
##   5.126421e-01  1.162632e-109 
## [1] "myfit_mdl: predict complete: 6.602000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 feats
## 1 nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                      1.488                 0.073
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5797928        0.556    0.6035857       0.6320319
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.3        0.678153        0.5236096
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5024844               0.56518    0.04708344
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5238678    0.4788732    0.5688623       0.5202713
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                      0       0.6684456         0.502004
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4705156             0.5334806             0
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01780093      0.03558361
## [1] "myfit_mdl: exit: 6.618000 secs"
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 207.080 213.741
## 5 fit.models_1_preProc          1          4     preProc 213.742      NA
##   elapsed
## 4   6.661
## 5      NA
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indep_vars_vctr
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indep_vars_vctr_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indep_vars_vctr_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indep_vars_vctr_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indep_vars_vctr=indep_vars_vctr,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  nImgs.cut.fctr,resY.min
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        nImgs.cut.fctr,resY.min
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                 nImgs.cut.fctr,resY.min,nImgs.cut.fctr:nImgs.cut.fctr,nImgs.cut.fctr:resX.mad.log1p,nImgs.cut.fctr:nImgs.log1p,nImgs.cut.fctr:resY.mean.log1p,nImgs.cut.fctr:resY.mean.root2,nImgs.cut.fctr:resXY.mad,nImgs.cut.fctr:resXY.mad.nexp,nImgs.cut.fctr:resX.mean,nImgs.cut.fctr:resX.mean.nexp,nImgs.cut.fctr:resX.min,nImgs.cut.fctr:resXY.min,nImgs.cut.fctr:resY.min
## Low.cor.X##rcv#glmnet                                                                                                                                                                                                                                                                                        nImgs.cut.fctr,.pos,resX.mad.log1p,resXY.mad.nexp,resY.mean.log1p,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad,nImgs,resX.mean,resX.min.nexp,resX.mean.nexp,resY.min.nexp,resX.min,resY.min
## All.X##rcv#glmnet               nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
## All.X##rcv#glm                  nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
##                                 max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                           0                      0.422
## Random###myrandom_classfr                     0                      0.343
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.780
## Max.cor.Y##rcv#rpart                          5                      1.690
## Interact.High.cor.Y##rcv#glmnet              25                     22.427
## Low.cor.X##rcv#glmnet                        25                      6.475
## All.X##rcv#glmnet                            25                     11.043
## All.X##rcv#glm                                1                      1.488
##                                 min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr                             0.002       0.5000000
## Random###myrandom_classfr                       0.002       0.4980239
## Max.cor.Y.rcv.1X1###glmnet                      0.017       0.5304143
## Max.cor.Y##rcv#rpart                            0.016       0.5818446
## Interact.High.cor.Y##rcv#glmnet                 1.069       0.6058048
## Low.cor.X##rcv#glmnet                           0.633       0.5638247
## All.X##rcv#glmnet                               1.193       0.5678247
## All.X##rcv#glm                                  0.073       0.5797928
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                    0.000    1.0000000       0.5000000
## Random###myrandom_classfr              0.508    0.4880478       0.4870120
## Max.cor.Y.rcv.1X1###glmnet             0.268    0.7928287       0.5392470
## Max.cor.Y##rcv#rpart                   0.586    0.5776892       0.5938127
## Interact.High.cor.Y##rcv#glmnet        0.614    0.5976096       0.6427490
## Low.cor.X##rcv#glmnet                  0.540    0.5876494       0.5929761
## All.X##rcv#glmnet                      0.548    0.5876494       0.6177371
## All.X##rcv#glm                         0.556    0.6035857       0.6320319
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                                0.4       0.6675532
## Random###myrandom_classfr                          0.4       0.6675532
## Max.cor.Y.rcv.1X1###glmnet                         0.4       0.6675532
## Max.cor.Y##rcv#rpart                               0.3       0.6698177
## Interact.High.cor.Y##rcv#glmnet                    0.3       0.6716519
## Low.cor.X##rcv#glmnet                              0.3       0.6724832
## All.X##rcv#glmnet                                  0.3       0.6784741
## All.X##rcv#glm                                     0.3       0.6781530
##                                 max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr                    0.5009980             0.4695761
## Random###myrandom_classfr              0.5009980             0.4695761
## Max.cor.Y.rcv.1X1###glmnet             0.5009980             0.4695761
## Max.cor.Y##rcv#rpart                   0.5255967             0.4805306
## Interact.High.cor.Y##rcv#glmnet        0.5448976             0.4865121
## Low.cor.X##rcv#glmnet                  0.5265996             0.4815272
## All.X##rcv#glmnet                      0.5299353             0.4974896
## All.X##rcv#glm                         0.5236096             0.5024844
##                                 max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr                         0.5324140    0.00000000
## Random###myrandom_classfr                   0.5324140    0.00000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5324140    0.00000000
## Max.cor.Y##rcv#rpart                        0.5433509    0.05146235
## Interact.High.cor.Y##rcv#glmnet             0.5493102    0.08975773
## Low.cor.X##rcv#glmnet                       0.5443444    0.05311189
## All.X##rcv#glmnet                           0.5602241    0.05971262
## All.X##rcv#glm                              0.5651800    0.04708344
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5059679    0.4949698    0.5169661
## Max.cor.Y.rcv.1X1###glmnet            0.5300104    0.2676056    0.7924152
## Max.cor.Y##rcv#rpart                  0.5039318    0.4849095    0.5229541
## Interact.High.cor.Y##rcv#glmnet       0.5040041    0.5030181    0.5049900
## Low.cor.X##rcv#glmnet                 0.4978875    0.4708249    0.5249501
## All.X##rcv#glmnet                     0.5108937    0.4788732    0.5429142
## All.X##rcv#glm                        0.5238678    0.4788732    0.5688623
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                    0.4
## Random###myrandom_classfr             0.4969618                    0.4
## Max.cor.Y.rcv.1X1###glmnet            0.5290506                    0.4
## Max.cor.Y##rcv#rpart                  0.4985602                    0.2
## Interact.High.cor.Y##rcv#glmnet       0.5140745                    0.2
## Low.cor.X##rcv#glmnet                 0.5012952                    0.0
## All.X##rcv#glmnet                     0.5204922                    0.0
## All.X##rcv#glm                        0.5202713                    0.0
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6684456         0.502004
## Random###myrandom_classfr             0.6684456         0.502004
## Max.cor.Y.rcv.1X1###glmnet            0.6684456         0.502004
## Max.cor.Y##rcv#rpart                  0.6684456         0.502004
## Interact.High.cor.Y##rcv#glmnet       0.6697987         0.507014
## Low.cor.X##rcv#glmnet                 0.6684456         0.502004
## All.X##rcv#glmnet                     0.6684456         0.502004
## All.X##rcv#glm                        0.6684456         0.502004
##                                 max.AccuracyLower.OOB
## MFO###myMFO_classfr                         0.4705156
## Random###myrandom_classfr                   0.4705156
## Max.cor.Y.rcv.1X1###glmnet                  0.4705156
## Max.cor.Y##rcv#rpart                        0.4705156
## Interact.High.cor.Y##rcv#glmnet             0.4755136
## Low.cor.X##rcv#glmnet                       0.4705156
## All.X##rcv#glmnet                           0.4705156
## All.X##rcv#glm                              0.4705156
##                                 max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr                         0.5334806    0.00000000
## Random###myrandom_classfr                   0.5334806    0.00000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5334806    0.00000000
## Max.cor.Y##rcv#rpart                        0.5334806    0.00000000
## Interact.High.cor.Y##rcv#glmnet             0.5384730    0.01013221
## Low.cor.X##rcv#glmnet                       0.5334806    0.00000000
## All.X##rcv#glmnet                           0.5334806    0.00000000
## All.X##rcv#glm                              0.5334806    0.00000000
##                                 max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr                             NA              NA
## Random###myrandom_classfr                       NA              NA
## Max.cor.Y.rcv.1X1###glmnet                      NA              NA
## Max.cor.Y##rcv#rpart                    0.02756072      0.05485113
## Interact.High.cor.Y##rcv#glmnet         0.02621502      0.05242679
## Low.cor.X##rcv#glmnet                   0.01957750      0.03917575
## All.X##rcv#glmnet                       0.02333108      0.04665017
## All.X##rcv#glm                          0.01780093      0.03558361
rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 213.742 213.831
## 6     fit.models_1_end          1          5    teardown 213.831      NA
##   elapsed
## 5   0.089
## 6      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 17 fit.models          8          1           1 186.824 213.841  27.017
## 18 fit.models          8          2           2 213.842      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 217.229  NA      NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  nImgs.cut.fctr,resY.min
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        nImgs.cut.fctr,resY.min
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                 nImgs.cut.fctr,resY.min,nImgs.cut.fctr:nImgs.cut.fctr,nImgs.cut.fctr:resX.mad.log1p,nImgs.cut.fctr:nImgs.log1p,nImgs.cut.fctr:resY.mean.log1p,nImgs.cut.fctr:resY.mean.root2,nImgs.cut.fctr:resXY.mad,nImgs.cut.fctr:resXY.mad.nexp,nImgs.cut.fctr:resX.mean,nImgs.cut.fctr:resX.mean.nexp,nImgs.cut.fctr:resX.min,nImgs.cut.fctr:resXY.min,nImgs.cut.fctr:resY.min
## Low.cor.X##rcv#glmnet                                                                                                                                                                                                                                                                                        nImgs.cut.fctr,.pos,resX.mad.log1p,resXY.mad.nexp,resY.mean.log1p,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad,nImgs,resX.mean,resX.min.nexp,resX.mean.nexp,resY.min.nexp,resX.min,resY.min
## All.X##rcv#glmnet               nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
## All.X##rcv#glm                  nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
##                                 max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr                           0       0.5000000
## Random###myrandom_classfr                     0       0.4980239
## Max.cor.Y.rcv.1X1###glmnet                    0       0.5304143
## Max.cor.Y##rcv#rpart                          5       0.5818446
## Interact.High.cor.Y##rcv#glmnet              25       0.6058048
## Low.cor.X##rcv#glmnet                        25       0.5638247
## All.X##rcv#glmnet                            25       0.5678247
## All.X##rcv#glm                                1       0.5797928
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                    0.000    1.0000000       0.5000000
## Random###myrandom_classfr              0.508    0.4880478       0.4870120
## Max.cor.Y.rcv.1X1###glmnet             0.268    0.7928287       0.5392470
## Max.cor.Y##rcv#rpart                   0.586    0.5776892       0.5938127
## Interact.High.cor.Y##rcv#glmnet        0.614    0.5976096       0.6427490
## Low.cor.X##rcv#glmnet                  0.540    0.5876494       0.5929761
## All.X##rcv#glmnet                      0.548    0.5876494       0.6177371
## All.X##rcv#glm                         0.556    0.6035857       0.6320319
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                                0.4       0.6675532
## Random###myrandom_classfr                          0.4       0.6675532
## Max.cor.Y.rcv.1X1###glmnet                         0.4       0.6675532
## Max.cor.Y##rcv#rpart                               0.3       0.6698177
## Interact.High.cor.Y##rcv#glmnet                    0.3       0.6716519
## Low.cor.X##rcv#glmnet                              0.3       0.6724832
## All.X##rcv#glmnet                                  0.3       0.6784741
## All.X##rcv#glm                                     0.3       0.6781530
##                                 max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr                    0.5009980    0.00000000
## Random###myrandom_classfr              0.5009980    0.00000000
## Max.cor.Y.rcv.1X1###glmnet             0.5009980    0.00000000
## Max.cor.Y##rcv#rpart                   0.5255967    0.05146235
## Interact.High.cor.Y##rcv#glmnet        0.5448976    0.08975773
## Low.cor.X##rcv#glmnet                  0.5265996    0.05311189
## All.X##rcv#glmnet                      0.5299353    0.05971262
## All.X##rcv#glm                         0.5236096    0.04708344
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5059679    0.4949698    0.5169661
## Max.cor.Y.rcv.1X1###glmnet            0.5300104    0.2676056    0.7924152
## Max.cor.Y##rcv#rpart                  0.5039318    0.4849095    0.5229541
## Interact.High.cor.Y##rcv#glmnet       0.5040041    0.5030181    0.5049900
## Low.cor.X##rcv#glmnet                 0.4978875    0.4708249    0.5249501
## All.X##rcv#glmnet                     0.5108937    0.4788732    0.5429142
## All.X##rcv#glm                        0.5238678    0.4788732    0.5688623
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                    0.4
## Random###myrandom_classfr             0.4969618                    0.4
## Max.cor.Y.rcv.1X1###glmnet            0.5290506                    0.4
## Max.cor.Y##rcv#rpart                  0.4985602                    0.2
## Interact.High.cor.Y##rcv#glmnet       0.5140745                    0.2
## Low.cor.X##rcv#glmnet                 0.5012952                    0.0
## All.X##rcv#glmnet                     0.5204922                    0.0
## All.X##rcv#glm                        0.5202713                    0.0
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6684456         0.502004
## Random###myrandom_classfr             0.6684456         0.502004
## Max.cor.Y.rcv.1X1###glmnet            0.6684456         0.502004
## Max.cor.Y##rcv#rpart                  0.6684456         0.502004
## Interact.High.cor.Y##rcv#glmnet       0.6697987         0.507014
## Low.cor.X##rcv#glmnet                 0.6684456         0.502004
## All.X##rcv#glmnet                     0.6684456         0.502004
## All.X##rcv#glm                        0.6684456         0.502004
##                                 max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr                0.00000000                 2.36966825
## Random###myrandom_classfr          0.00000000                 2.91545190
## Max.cor.Y.rcv.1X1###glmnet         0.00000000                 1.28205128
## Max.cor.Y##rcv#rpart               0.00000000                 0.59171598
## Interact.High.cor.Y##rcv#glmnet    0.01013221                 0.04458911
## Low.cor.X##rcv#glmnet              0.00000000                 0.15444015
## All.X##rcv#glmnet                  0.00000000                 0.09055510
## All.X##rcv#glm                     0.00000000                 0.67204301
##                                 inv.elapsedtime.final
## MFO###myMFO_classfr                       500.0000000
## Random###myrandom_classfr                 500.0000000
## Max.cor.Y.rcv.1X1###glmnet                 58.8235294
## Max.cor.Y##rcv#rpart                       62.5000000
## Interact.High.cor.Y##rcv#glmnet             0.9354537
## Low.cor.X##rcv#glmnet                       1.5797788
## All.X##rcv#glmnet                           0.8382230
## All.X##rcv#glm                             13.6986301
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 5 Interact.High.cor.Y##rcv#glmnet         0.507014       0.5140745
## 3      Max.cor.Y.rcv.1X1###glmnet         0.502004       0.5290506
## 7               All.X##rcv#glmnet         0.502004       0.5204922
## 8                  All.X##rcv#glm         0.502004       0.5202713
## 6           Low.cor.X##rcv#glmnet         0.502004       0.5012952
## 1             MFO###myMFO_classfr         0.502004       0.5000000
## 4            Max.cor.Y##rcv#rpart         0.502004       0.4985602
## 2       Random###myrandom_classfr         0.502004       0.4969618
##   max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 5       0.5040041        0.5448976                    0.3
## 3       0.5300104        0.5009980                    0.4
## 7       0.5108937        0.5299353                    0.3
## 8       0.5238678        0.5236096                    0.3
## 6       0.4978875        0.5265996                    0.3
## 1       0.5000000        0.5009980                    0.4
## 4       0.5039318        0.5255967                    0.3
## 2       0.5059679        0.5009980                    0.4
##   opt.prob.threshold.OOB
## 5                    0.2
## 3                    0.4
## 7                    0.0
## 8                    0.0
## 6                    0.0
## 1                    0.4
## 4                    0.2
## 2                    0.4
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fc77ac162d0>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: Interact.High.cor.Y##rcv#glmnet"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indep_vars <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indep_vars <- paste(indep_vars, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indep_vars <- intersect(indep_vars, names(glbObsFit))
    
#     indep_vars <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
#     if (glb_is_classification && glb_is_binomial)
#         indep_vars <- grep("prob$", indep_vars, value=TRUE) else
#         indep_vars <- indep_vars[!grepl("err$", indep_vars)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indep_vars)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indep_vars = indep_vars, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glb_sel_mdl_id)) 
    glb_sel_mdl_id <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glb_sel_mdl_id))   
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        4600   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        46   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                      .pos 
##             -5.322001e+02              1.037459e-04 
##                    .rnorm                     nImgs 
##             -6.769060e-02              1.521544e-04 
##     nImgs.cut.fctr(32,60]    nImgs.cut.fctr(60,120] 
##              1.309604e-01              6.523010e-02 
## nImgs.cut.fctr(120,3e+03]               nImgs.log1p 
##              3.460511e-01              5.930509e-01 
##                nImgs.nexp               nImgs.root2 
##              5.294506e+01             -1.033055e-01 
##                  resX.mad            resX.mad.log1p 
##             -7.462293e-03              3.347949e-01 
##             resX.mad.nexp            resX.mad.root2 
##              8.597647e-01              1.847927e-02 
##                 resX.mean           resX.mean.log1p 
##             -2.908604e-02              1.718262e+01 
##            resX.mean.nexp           resX.mean.root2 
##              9.900000e+35             -1.044154e-01 
##                  resX.min            resX.min.log1p 
##              1.111873e-02             -9.111545e+00 
##             resX.min.nexp            resX.min.root2 
##             -3.246667e+27              7.845843e-01 
##                 resXY.mad           resXY.mad.log1p 
##              2.701393e-06              1.155758e-01 
##            resXY.mad.nexp           resXY.mad.root2 
##              1.030157e+00             -2.916969e-03 
##                 resXY.max           resXY.max.log1p 
##             -1.265518e-04              3.573686e+01 
##           resXY.max.root2                resXY.mean 
##             -3.382568e-02             -1.511560e-04 
##          resXY.mean.log1p          resXY.mean.root2 
##              1.984867e+01             -6.256731e-03 
##                 resXY.min           resXY.min.log1p 
##             -3.496593e-05              1.785193e+00 
##           resXY.min.root2                  resY.mad 
##              8.746218e-03              1.065394e-02 
##            resY.mad.log1p             resY.mad.nexp 
##              1.160382e+00              1.082350e+00 
##            resY.mad.root2                 resY.mean 
##             -5.316544e-01              7.943836e-02 
##           resY.mean.log1p            resY.mean.nexp 
##             -2.938950e+01              9.900000e+35 
##           resY.mean.root2                  resY.min 
##              1.030505e-01              9.792523e-04 
##            resY.min.log1p             resY.min.nexp 
##             -2.872098e+00             -6.257821e+21 
##            resY.min.root2 
##              3.282024e-01 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"               ".pos"                     
##  [3] ".rnorm"                    "nImgs"                    
##  [5] "nImgs.cut.fctr(32,60]"     "nImgs.cut.fctr(60,120]"   
##  [7] "nImgs.cut.fctr(120,3e+03]" "nImgs.log1p"              
##  [9] "nImgs.nexp"                "nImgs.root2"              
## [11] "resX.mad"                  "resX.mad.log1p"           
## [13] "resX.mad.nexp"             "resX.mad.root2"           
## [15] "resX.mean"                 "resX.mean.log1p"          
## [17] "resX.mean.nexp"            "resX.mean.root2"          
## [19] "resX.min"                  "resX.min.log1p"           
## [21] "resX.min.nexp"             "resX.min.root2"           
## [23] "resXY.mad"                 "resXY.mad.log1p"          
## [25] "resXY.mad.nexp"            "resXY.mad.root2"          
## [27] "resXY.max"                 "resXY.max.log1p"          
## [29] "resXY.max.root2"           "resXY.mean"               
## [31] "resXY.mean.log1p"          "resXY.mean.root2"         
## [33] "resXY.min"                 "resXY.min.log1p"          
## [35] "resXY.min.root2"           "resY.mad"                 
## [37] "resY.mad.log1p"            "resY.mad.nexp"            
## [39] "resY.mad.root2"            "resY.mean"                
## [41] "resY.mean.log1p"           "resY.mean.nexp"           
## [43] "resY.mean.root2"           "resY.min"                 
## [45] "resY.min.log1p"            "resY.min.nexp"            
## [47] "resY.min.root2"
## [1] TRUE
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##                           All.X..rcv.glmnet.imp          imp
## resX.mean.nexp                     1.000000e+02 1.000000e+02
## resY.mean.nexp                     1.000000e+02 1.000000e+02
## resX.min.nexp                      3.279461e-07 3.279461e-07
## resY.min.nexp                      6.321031e-13 6.321031e-13
## nImgs.nexp                         5.347986e-33 5.347986e-33
## resXY.max.log1p                    3.609783e-33 3.609783e-33
## resY.mean.log1p                    2.968636e-33 2.968636e-33
## resXY.mean.log1p                   2.004916e-33 2.004916e-33
## resX.mean.log1p                    1.735618e-33 1.735618e-33
## resX.min.log1p                     9.203578e-34 9.203578e-34
## resY.min.log1p                     2.901106e-34 2.901106e-34
## resXY.min.log1p                    1.803222e-34 1.803222e-34
## resY.mad.log1p                     1.172100e-34 1.172100e-34
## resY.mad.nexp                      1.093280e-34 1.093280e-34
## resXY.mad.nexp                     1.040560e-34 1.040560e-34
## resX.mad.nexp                      8.684464e-35 8.684464e-35
## resX.min.root2                     7.925067e-35 7.925067e-35
## nImgs.log1p                        5.990386e-35 5.990386e-35
## resY.mad.root2                     5.370220e-35 5.370220e-35
## nImgs.cut.fctr(120,3e+03]          3.495438e-35 3.495438e-35
## resX.mad.log1p                     3.381740e-35 3.381740e-35
## resY.min.root2                     3.315149e-35 3.315149e-35
## nImgs.cut.fctr(32,60]              1.322805e-35 1.322805e-35
## resXY.mad.log1p                    1.167405e-35 1.167405e-35
## resX.mean.root2                    1.054674e-35 1.054674e-35
## nImgs.root2                        1.043463e-35 1.043463e-35
## resY.mean.root2                    1.040887e-35 1.040887e-35
## resY.mean                          8.023804e-36 8.023804e-36
## .rnorm                             6.837161e-36 6.837161e-36
## nImgs.cut.fctr(60,120]             6.588626e-36 6.588626e-36
## resXY.max.root2                    3.416462e-36 3.416462e-36
## resX.mean                          2.937711e-36 2.937711e-36
## resX.mad.root2                     1.866320e-36 1.866320e-36
## resX.min                           1.122832e-36 1.122832e-36
## resY.mad                           1.075883e-36 1.075883e-36
## resXY.min.root2                    8.831835e-37 8.831835e-37
## resX.mad                           7.534941e-37 7.534941e-37
## resXY.mean.root2                   6.317202e-37 6.317202e-37
## resXY.mad.root2                    2.943704e-37 2.943704e-37
## resY.min                           9.864150e-38 9.864150e-38
## nImgs                              1.509626e-38 1.509626e-38
## resXY.mean                         1.499542e-38 1.499542e-38
## resXY.max                          1.251014e-38 1.251014e-38
## .pos                               1.020651e-38 1.020651e-38
## resXY.min                          3.259045e-39 3.259045e-39
## resXY.mad                          0.000000e+00 0.000000e+00
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
            prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 44

## [1] "Min/Max Boundaries: "
##   business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 1         710            Y                          0.47295347
## 2         286            N                          0.07153311
## 3        2846            N                          0.99830797
##   outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 1                              Y                              FALSE
## 2                              Y                               TRUE
## 3                              Y                               TRUE
##   outdoor.fctr.All.X..rcv.glmnet.err.abs
## 1                             0.52704653
## 2                             0.07153311
## 3                             0.99830797
##   outdoor.fctr.All.X..rcv.glmnet.is.acc
## 1                                  TRUE
## 2                                 FALSE
## 3                                 FALSE
##   outdoor.fctr.All.X..rcv.glmnet.accurate
## 1                                    TRUE
## 2                                   FALSE
## 3                                   FALSE
##   outdoor.fctr.All.X..rcv.glmnet.error .label
## 1                           0.00000000    710
## 2                           0.07153311    286
## 3                           0.99830797   2846
## [1] "Inaccurate: "
##   business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 1        3945            N                          0.00000000
## 2        1634            N                          0.00000000
## 3         286            N                          0.07153311
## 4        1402            N                          0.15396397
## 5        2495            N                          0.17075236
## 6          77            N                          0.18236736
##   outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 1                              Y                               TRUE
## 2                              Y                               TRUE
## 3                              Y                               TRUE
## 4                              Y                               TRUE
## 5                              Y                               TRUE
## 6                              Y                               TRUE
##   outdoor.fctr.All.X..rcv.glmnet.err.abs
## 1                             0.00000000
## 2                             0.00000000
## 3                             0.07153311
## 4                             0.15396397
## 5                             0.17075236
## 6                             0.18236736
##   outdoor.fctr.All.X..rcv.glmnet.is.acc
## 1                                 FALSE
## 2                                 FALSE
## 3                                 FALSE
## 4                                 FALSE
## 5                                 FALSE
## 6                                 FALSE
##   outdoor.fctr.All.X..rcv.glmnet.accurate
## 1                                   FALSE
## 2                                   FALSE
## 3                                   FALSE
## 4                                   FALSE
## 5                                   FALSE
## 6                                   FALSE
##   outdoor.fctr.All.X..rcv.glmnet.error
## 1                           0.00000000
## 2                           0.00000000
## 3                           0.07153311
## 4                           0.15396397
## 5                           0.17075236
## 6                           0.18236736
##     business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 107        2671            N                           0.4131585
## 122        3313            N                           0.4214526
## 213        3896            N                           0.4886172
## 242        3376            N                           0.5051562
## 272        2104            N                           0.5180997
## 413        3487            N                           0.5989197
##     outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 107                              Y                               TRUE
## 122                              Y                               TRUE
## 213                              Y                               TRUE
## 242                              Y                               TRUE
## 272                              Y                               TRUE
## 413                              Y                               TRUE
##     outdoor.fctr.All.X..rcv.glmnet.err.abs
## 107                              0.4131585
## 122                              0.4214526
## 213                              0.4886172
## 242                              0.5051562
## 272                              0.5180997
## 413                              0.5989197
##     outdoor.fctr.All.X..rcv.glmnet.is.acc
## 107                                 FALSE
## 122                                 FALSE
## 213                                 FALSE
## 242                                 FALSE
## 272                                 FALSE
## 413                                 FALSE
##     outdoor.fctr.All.X..rcv.glmnet.accurate
## 107                                   FALSE
## 122                                   FALSE
## 213                                   FALSE
## 242                                   FALSE
## 272                                   FALSE
## 413                                   FALSE
##     outdoor.fctr.All.X..rcv.glmnet.error
## 107                            0.4131585
## 122                            0.4214526
## 213                            0.4886172
## 242                            0.5051562
## 272                            0.5180997
## 413                            0.5989197
##     business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 492        3980            N                           0.7334074
## 493        2646            N                           0.7447127
## 494        3018            N                           0.7467896
## 495         574            N                           0.7489144
## 496         393            N                           0.7580598
## 497        2846            N                           0.9983080
##     outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 492                              Y                               TRUE
## 493                              Y                               TRUE
## 494                              Y                               TRUE
## 495                              Y                               TRUE
## 496                              Y                               TRUE
## 497                              Y                               TRUE
##     outdoor.fctr.All.X..rcv.glmnet.err.abs
## 492                              0.7334074
## 493                              0.7447127
## 494                              0.7467896
## 495                              0.7489144
## 496                              0.7580598
## 497                              0.9983080
##     outdoor.fctr.All.X..rcv.glmnet.is.acc
## 492                                 FALSE
## 493                                 FALSE
## 494                                 FALSE
## 495                                 FALSE
## 496                                 FALSE
## 497                                 FALSE
##     outdoor.fctr.All.X..rcv.glmnet.accurate
## 492                                   FALSE
## 493                                   FALSE
## 494                                   FALSE
## 495                                   FALSE
## 496                                   FALSE
## 497                                   FALSE
##     outdoor.fctr.All.X..rcv.glmnet.error
## 492                            0.7334074
## 493                            0.7447127
## 494                            0.7467896
## 495                            0.7489144
## 496                            0.7580598
## 497                            0.9983080

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##             nImgs.cut.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## (32,60]            (32,60]    243    278   2512      0.2774451
## (60,120]          (60,120]    260    257   2459      0.2564870
## (0,32]              (0,32]    237    238   2532      0.2375250
## (120,3e+03]    (120,3e+03]    258    229   2497      0.2285429
##             .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## (32,60]          0.2434870         0.2512        134.8544        0.4850879
## (60,120]         0.2605210         0.2459        123.8999        0.4821008
## (0,32]           0.2374749         0.2532        109.1638        0.4586716
## (120,3e+03]      0.2585170         0.2497        108.0508        0.4718376
##             .n.fit err.abs.OOB.sum err.abs.OOB.mean
## (32,60]        278        122.3399        0.5034566
## (60,120]       257        129.2920        0.4972768
## (0,32]         238        117.2849        0.4948730
## (120,3e+03]    229        126.8451        0.4916477
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       998.000000      1002.000000     10000.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       475.969008         1.897698 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      1002.000000       495.761912         1.987254
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glb_sel_mdl_id), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 224.745  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 18 fit.models          8          2           2 213.842 224.756  10.914
## 19 fit.models          8          3           3 224.757      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 19        fit.models          8          3           3 224.757 229.013
## 20 fit.data.training          9          0           0 229.013      NA
##    elapsed
## 19   4.256
## 20      NA

Step 9.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glb_sel_mdl_id")
    glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indep_vars <- myadjust_interaction_feats(subset(glb_feats_df, 
                                                    !nzv & (exclude.as.feat != 1))[, "id"])
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indep_vars, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indep_vars = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
        
    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
        indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indep_vars_vctr <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glb_sel_mdl_id
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glb_sel_mdl_id)) != -1))
        ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indep_vars: nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min"
## [1] "myfit_mdl: setup complete: 0.738000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00236 on full training set
## [1] "myfit_mdl: train complete: 22.713000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            50   -none-     numeric  
## beta        2300   dgCMatrix  S4       
## df            50   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        50   -none-     numeric  
## dev.ratio     50   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        46   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##           (Intercept)                  .pos                .rnorm 
##         -3.063087e+00          7.035722e-05         -5.454361e-03 
##                 nImgs nImgs.cut.fctr(32,60]           nImgs.log1p 
##         -9.785444e-04          1.150942e-01          2.553484e-01 
##            nImgs.nexp        resX.mad.log1p        resX.mean.nexp 
##          1.920825e+00          9.324753e-03         -9.900000e+35 
##              resX.min         resX.min.nexp             resXY.max 
##          2.707822e-04         -6.916709e+26         -2.975932e-06 
##            resXY.mean             resXY.min              resY.mad 
##         -1.801540e-07         -1.103577e-06          1.487094e-04 
##         resY.mad.nexp       resY.mean.log1p        resY.mean.nexp 
##          2.248354e-02          4.798175e-01         -9.900000e+35 
##              resY.min         resY.min.nexp 
##         -5.930960e-04         -9.302222e+12 
## [1] "max lambda < lambdaOpt:"
##           (Intercept)                  .pos                .rnorm 
##         -3.344275e+00          7.183591e-05         -6.355870e-03 
##                 nImgs nImgs.cut.fctr(32,60]           nImgs.log1p 
##         -1.002766e-03          1.181961e-01          2.620960e-01 
##            nImgs.nexp        resX.mad.log1p        resX.mean.nexp 
##          2.259889e+00          9.110044e-03         -9.900000e+35 
##              resX.min         resX.min.nexp             resXY.max 
##          3.549463e-04         -7.206478e+26         -3.073427e-06 
##            resXY.mean             resXY.min              resY.mad 
##         -4.916887e-07         -1.216240e-06          2.113887e-04 
##         resY.mad.nexp       resY.mean.log1p        resY.mean.nexp 
##          2.948711e-02          5.314093e-01         -9.900000e+35 
##              resY.min         resY.min.nexp 
##         -5.776209e-04         -9.728555e+12 
## [1] "myfit_mdl: train diagnostics complete: 23.305000 secs"

##          Prediction
## Reference   N   Y
##         N  42 955
##         Y  19 984
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.130000e-01   2.324849e-02   4.908389e-01   5.351230e-01   5.015000e-01 
## AccuracyPValue  McnemarPValue 
##   1.571511e-01  3.325830e-197 
## [1] "myfit_mdl: predict complete: 25.347000 secs"
##                  id
## 1 Final##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 feats
## 1 nImgs.cut.fctr,nImgs.log1p,.pos,resX.mad.log1p,resX.mad.root2,resX.mad,resXY.mad.nexp,nImgs.root2,resY.mean.log1p,resY.mean.root2,resY.mean,resY.mad.nexp,resY.mad,resXY.max.log1p,resXY.max.root2,resXY.max,resY.mad.root2,resY.mad.log1p,nImgs.nexp,resXY.mean.log1p,resXY.mean.root2,.rnorm,resXY.mean,resXY.mad.root2,resXY.mad,resX.mad.nexp,resXY.mad.log1p,nImgs,resX.mean.log1p,resX.mean.root2,resX.mean,resX.min.nexp,resX.mean.nexp,resY.mean.nexp,resY.min.nexp,resX.min.log1p,resX.min.root2,resX.min,resXY.min.log1p,resXY.min.root2,resY.min.log1p,resY.min.root2,resXY.min,resY.min
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     21.952                  0.35
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5367168     0.442327    0.6311067        0.559857
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.6689327        0.5109885
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4908389              0.535123    0.02161427
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01801986      0.03607316
## [1] "myfit_mdl: exit: 25.363000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 20 fit.data.training          9          0           0 229.013 254.864
## 21 fit.data.training          9          1           1 254.865      NA
##    elapsed
## 20  25.851
## 21      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glb_fin_mdl_id)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glb_fin_mdl_id)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, :
## Using default probability threshold: 0
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                           All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## resX.mean.nexp                     1.000000e+02          1.000000e+02
## resY.mean.nexp                     1.000000e+02          1.000000e+02
## resX.min.nexp                      3.279461e-07          7.150779e-08
## resY.min.nexp                      6.321031e-13          9.637774e-22
## nImgs.nexp                         5.347986e-33          2.132364e-34
## resY.mean.log1p                    2.968636e-33          5.138997e-35
## nImgs.log1p                        5.990386e-35          2.617513e-35
## nImgs.cut.fctr(32,60]              1.322805e-35          1.180146e-35
## resY.mad.nexp                      1.093280e-34          2.667936e-36
## resX.mad.log1p                     3.381740e-35          9.297274e-37
## .rnorm                             6.837161e-36          6.020313e-37
## nImgs                              1.509626e-38          1.002154e-37
## resY.min                           9.864150e-38          5.903176e-38
## resX.min                           1.122832e-36          3.212106e-38
## resY.mad                           1.075883e-36          1.857300e-38
## .pos                               1.020651e-38          7.190583e-39
## resXY.max                          1.251014e-38          3.061240e-40
## resXY.min                          3.259045e-39          1.178567e-40
## resXY.mean                         1.499542e-38          3.585110e-41
## nImgs.cut.fctr(120,3e+03]          3.495438e-35          0.000000e+00
## nImgs.cut.fctr(60,120]             6.588626e-36          0.000000e+00
## nImgs.root2                        1.043463e-35          0.000000e+00
## resX.mad                           7.534941e-37          0.000000e+00
## resX.mad.nexp                      8.684464e-35          0.000000e+00
## resX.mad.root2                     1.866320e-36          0.000000e+00
## resX.mean                          2.937711e-36          0.000000e+00
## resX.mean.log1p                    1.735618e-33          0.000000e+00
## resX.mean.root2                    1.054674e-35          0.000000e+00
## resX.min.log1p                     9.203578e-34          0.000000e+00
## resX.min.root2                     7.925067e-35          0.000000e+00
## resXY.mad                          0.000000e+00          0.000000e+00
## resXY.mad.log1p                    1.167405e-35          0.000000e+00
## resXY.mad.nexp                     1.040560e-34          0.000000e+00
## resXY.mad.root2                    2.943704e-37          0.000000e+00
## resXY.max.log1p                    3.609783e-33          0.000000e+00
## resXY.max.root2                    3.416462e-36          0.000000e+00
## resXY.mean.log1p                   2.004916e-33          0.000000e+00
## resXY.mean.root2                   6.317202e-37          0.000000e+00
## resXY.min.log1p                    1.803222e-34          0.000000e+00
## resXY.min.root2                    8.831835e-37          0.000000e+00
## resY.mad.log1p                     1.172100e-34          0.000000e+00
## resY.mad.root2                     5.370220e-35          0.000000e+00
## resY.mean                          8.023804e-36          0.000000e+00
## resY.mean.root2                    1.040887e-35          0.000000e+00
## resY.min.log1p                     2.901106e-34          0.000000e+00
## resY.min.root2                     3.315149e-35          0.000000e+00
##                                    imp
## resX.mean.nexp            1.000000e+02
## resY.mean.nexp            1.000000e+02
## resX.min.nexp             7.150779e-08
## resY.min.nexp             9.637774e-22
## nImgs.nexp                2.132364e-34
## resY.mean.log1p           5.138997e-35
## nImgs.log1p               2.617513e-35
## nImgs.cut.fctr(32,60]     1.180146e-35
## resY.mad.nexp             2.667936e-36
## resX.mad.log1p            9.297274e-37
## .rnorm                    6.020313e-37
## nImgs                     1.002154e-37
## resY.min                  5.903176e-38
## resX.min                  3.212106e-38
## resY.mad                  1.857300e-38
## .pos                      7.190583e-39
## resXY.max                 3.061240e-40
## resXY.min                 1.178567e-40
## resXY.mean                3.585110e-41
## nImgs.cut.fctr(120,3e+03] 0.000000e+00
## nImgs.cut.fctr(60,120]    0.000000e+00
## nImgs.root2               0.000000e+00
## resX.mad                  0.000000e+00
## resX.mad.nexp             0.000000e+00
## resX.mad.root2            0.000000e+00
## resX.mean                 0.000000e+00
## resX.mean.log1p           0.000000e+00
## resX.mean.root2           0.000000e+00
## resX.min.log1p            0.000000e+00
## resX.min.root2            0.000000e+00
## resXY.mad                 0.000000e+00
## resXY.mad.log1p           0.000000e+00
## resXY.mad.nexp            0.000000e+00
## resXY.mad.root2           0.000000e+00
## resXY.max.log1p           0.000000e+00
## resXY.max.root2           0.000000e+00
## resXY.mean.log1p          0.000000e+00
## resXY.mean.root2          0.000000e+00
## resXY.min.log1p           0.000000e+00
## resXY.min.root2           0.000000e+00
## resY.mad.log1p            0.000000e+00
## resY.mad.root2            0.000000e+00
## resY.mean                 0.000000e+00
## resY.mean.root2           0.000000e+00
## resY.min.log1p            0.000000e+00
## resY.min.root2            0.000000e+00
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id, 
            prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 44

## [1] "Min/Max Boundaries: "
##   business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 1         710            Y                                  NA
## 2        2846            N                                  NA
## 3         286            N                                  NA
##   outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 1                           <NA>                                 NA
## 2                           <NA>                                 NA
## 3                           <NA>                                 NA
##   outdoor.fctr.All.X..rcv.glmnet.err.abs
## 1                                     NA
## 2                                     NA
## 3                                     NA
##   outdoor.fctr.All.X..rcv.glmnet.is.acc
## 1                                    NA
## 2                                    NA
## 3                                    NA
##   outdoor.fctr.Final..rcv.glmnet.prob outdoor.fctr.Final..rcv.glmnet
## 1                           0.4019945                              Y
## 2                           0.3788056                              Y
## 3                           0.3900328                              Y
##   outdoor.fctr.Final..rcv.glmnet.err
## 1                              FALSE
## 2                               TRUE
## 3                               TRUE
##   outdoor.fctr.Final..rcv.glmnet.err.abs
## 1                              0.5980055
## 2                              0.3788056
## 3                              0.3900328
##   outdoor.fctr.Final..rcv.glmnet.is.acc
## 1                                  TRUE
## 2                                 FALSE
## 3                                 FALSE
##   outdoor.fctr.Final..rcv.glmnet.accurate
## 1                                    TRUE
## 2                                   FALSE
## 3                                   FALSE
##   outdoor.fctr.Final..rcv.glmnet.error .label
## 1                            0.0000000    710
## 2                            0.3788056   2846
## 3                            0.3900328    286
## [1] "Inaccurate: "
##   business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 1        1634            N                                  NA
## 2        1445            N                        0.0007709958
## 3        2339            N                        0.4216553578
## 4        1114            N                        0.2697178532
## 5        3065            N                        0.1225230650
## 6        2611            N                        0.2918475560
##   outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 1                           <NA>                                 NA
## 2                              Y                               TRUE
## 3                              Y                               TRUE
## 4                              Y                               TRUE
## 5                              Y                               TRUE
## 6                              Y                               TRUE
##   outdoor.fctr.All.X..rcv.glmnet.err.abs
## 1                                     NA
## 2                           0.0007709958
## 3                           0.4216553578
## 4                           0.2697178532
## 5                           0.1225230650
## 6                           0.2918475560
##   outdoor.fctr.All.X..rcv.glmnet.is.acc
## 1                                    NA
## 2                                 FALSE
## 3                                 FALSE
## 4                                 FALSE
## 5                                 FALSE
## 6                                 FALSE
##   outdoor.fctr.Final..rcv.glmnet.prob outdoor.fctr.Final..rcv.glmnet
## 1                           0.1055817                              Y
## 2                           0.1065867                              Y
## 3                           0.1372385                              Y
## 4                           0.1392711                              Y
## 5                           0.2485974                              Y
## 6                           0.2693462                              Y
##   outdoor.fctr.Final..rcv.glmnet.err
## 1                               TRUE
## 2                               TRUE
## 3                               TRUE
## 4                               TRUE
## 5                               TRUE
## 6                               TRUE
##   outdoor.fctr.Final..rcv.glmnet.err.abs
## 1                              0.1055817
## 2                              0.1065867
## 3                              0.1372385
## 4                              0.1392711
## 5                              0.2485974
## 6                              0.2693462
##   outdoor.fctr.Final..rcv.glmnet.is.acc
## 1                                 FALSE
## 2                                 FALSE
## 3                                 FALSE
## 4                                 FALSE
## 5                                 FALSE
## 6                                 FALSE
##   outdoor.fctr.Final..rcv.glmnet.accurate
## 1                                   FALSE
## 2                                   FALSE
## 3                                   FALSE
## 4                                   FALSE
## 5                                   FALSE
## 6                                   FALSE
##   outdoor.fctr.Final..rcv.glmnet.error
## 1                            0.1055817
## 2                            0.1065867
## 3                            0.1372385
## 4                            0.1392711
## 5                            0.2485974
## 6                            0.2693462
##     business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 97         3849            N                                  NA
## 231        2971            N                                  NA
## 266        1796            N                                  NA
## 267        3403            N                                  NA
## 440         590            N                           0.5656691
## 732        1378            N                                  NA
##     outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 97                            <NA>                                 NA
## 231                           <NA>                                 NA
## 266                           <NA>                                 NA
## 267                           <NA>                                 NA
## 440                              Y                               TRUE
## 732                           <NA>                                 NA
##     outdoor.fctr.All.X..rcv.glmnet.err.abs
## 97                                      NA
## 231                                     NA
## 266                                     NA
## 267                                     NA
## 440                              0.5656691
## 732                                     NA
##     outdoor.fctr.All.X..rcv.glmnet.is.acc
## 97                                     NA
## 231                                    NA
## 266                                    NA
## 267                                    NA
## 440                                 FALSE
## 732                                    NA
##     outdoor.fctr.Final..rcv.glmnet.prob outdoor.fctr.Final..rcv.glmnet
## 97                            0.4253303                              Y
## 231                           0.4673646                              Y
## 266                           0.4744031                              Y
## 267                           0.4744499                              Y
## 440                           0.4998805                              Y
## 732                           0.5297045                              Y
##     outdoor.fctr.Final..rcv.glmnet.err
## 97                                TRUE
## 231                               TRUE
## 266                               TRUE
## 267                               TRUE
## 440                               TRUE
## 732                               TRUE
##     outdoor.fctr.Final..rcv.glmnet.err.abs
## 97                               0.4253303
## 231                              0.4673646
## 266                              0.4744031
## 267                              0.4744499
## 440                              0.4998805
## 732                              0.5297045
##     outdoor.fctr.Final..rcv.glmnet.is.acc
## 97                                  FALSE
## 231                                 FALSE
## 266                                 FALSE
## 267                                 FALSE
## 440                                 FALSE
## 732                                 FALSE
##     outdoor.fctr.Final..rcv.glmnet.accurate
## 97                                    FALSE
## 231                                   FALSE
## 266                                   FALSE
## 267                                   FALSE
## 440                                   FALSE
## 732                                   FALSE
##     outdoor.fctr.Final..rcv.glmnet.error
## 97                             0.4253303
## 231                            0.4673646
## 266                            0.4744031
## 267                            0.4744499
## 440                            0.4998805
## 732                            0.5297045
##     business_id outdoor.fctr outdoor.fctr.All.X..rcv.glmnet.prob
## 992         615            N                           0.4612589
## 993         955            N                                  NA
## 994        3737            N                           0.3893913
## 995         941            N                                  NA
## 996        3858            N                           0.2536333
## 997        3285            N                           0.7940934
##     outdoor.fctr.All.X..rcv.glmnet outdoor.fctr.All.X..rcv.glmnet.err
## 992                              Y                               TRUE
## 993                           <NA>                                 NA
## 994                              Y                               TRUE
## 995                           <NA>                                 NA
## 996                              Y                               TRUE
## 997                              Y                               TRUE
##     outdoor.fctr.All.X..rcv.glmnet.err.abs
## 992                              0.4612589
## 993                                     NA
## 994                              0.3893913
## 995                                     NA
## 996                              0.2536333
## 997                              0.7940934
##     outdoor.fctr.All.X..rcv.glmnet.is.acc
## 992                                 FALSE
## 993                                    NA
## 994                                 FALSE
## 995                                    NA
## 996                                 FALSE
## 997                                 FALSE
##     outdoor.fctr.Final..rcv.glmnet.prob outdoor.fctr.Final..rcv.glmnet
## 992                           0.5890333                              Y
## 993                           0.5920903                              Y
## 994                           0.5930546                              Y
## 995                           0.5957571                              Y
## 996                           0.6010328                              Y
## 997                           0.6023388                              Y
##     outdoor.fctr.Final..rcv.glmnet.err
## 992                               TRUE
## 993                               TRUE
## 994                               TRUE
## 995                               TRUE
## 996                               TRUE
## 997                               TRUE
##     outdoor.fctr.Final..rcv.glmnet.err.abs
## 992                              0.5890333
## 993                              0.5920903
## 994                              0.5930546
## 995                              0.5957571
## 996                              0.6010328
## 997                              0.6023388
##     outdoor.fctr.Final..rcv.glmnet.is.acc
## 992                                 FALSE
## 993                                 FALSE
## 994                                 FALSE
## 995                                 FALSE
## 996                                 FALSE
## 997                                 FALSE
##     outdoor.fctr.Final..rcv.glmnet.accurate
## 992                                   FALSE
## 993                                   FALSE
## 994                                   FALSE
## 995                                   FALSE
## 996                                   FALSE
## 997                                   FALSE
##     outdoor.fctr.Final..rcv.glmnet.error
## 992                            0.5890333
## 993                            0.5920903
## 994                            0.5930546
## 995                            0.5957571
## 996                            0.6010328
## 997                            0.6023388

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "outdoor.fctr.Final..rcv.glmnet.prob"   
## [2] "outdoor.fctr.Final..rcv.glmnet"        
## [3] "outdoor.fctr.Final..rcv.glmnet.err"    
## [4] "outdoor.fctr.Final..rcv.glmnet.err.abs"
## [5] "outdoor.fctr.Final..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 21 fit.data.training          9          1           1 254.865 261.304
## 22  predict.data.new         10          0           0 261.304      NA
##    elapsed
## 21   6.439
## 22      NA

Step 10.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0

## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 44
## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## Warning: Removed 10000 rows containing missing values (geom_point).

## NULL
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs outdoor.fctr.All.X..rcv.glmnet Y: min < min of Train range: 25"
##      business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 2           1001                              Y    2  449.2222
## 254         1402                              Y  254  329.3333
## 662         2146                              Y  662  414.7059
## 879         2562                              Y  879  492.8571
## 1027        2846                              Y 1027  500.0000
## 1036         286                              Y 1036  283.7692
##      resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min.nexp
## 2           6.109741  8.040216e-196        21.19486 3.331006e-156
## 254         5.800102  9.383193e-144        18.14754  3.342796e-80
## 662         6.029978  7.861831e-181        20.36433 5.148200e-131
## 879         6.202246  9.012857e-215        22.20039 1.915170e-174
## 1027        6.216606  7.124576e-218        22.36068 7.124576e-218
## 1036        5.651679  5.762208e-124        16.84545  2.053885e-85
##      resXY.mean resXY.mean.log1p resXY.mean.root2 resXY.min
## 2     187964.00         12.14401         433.5482    179000
## 254   115110.00         11.65365         339.2786     27985
## 662   144558.82         11.88145         380.2089     67500
## 879   168107.14         12.03236         410.0087    120000
## 1027  191250.00         12.16134         437.3214    187500
## 1036   87626.15         11.38085         296.0172     25350
##      resXY.min.log1p resXY.min.root2 resY.mad.nexp resY.mean
## 2           12.09515        423.0839  9.674088e-10  426.4444
## 254         10.23946        167.2872  3.857670e-36  322.5333
## 662         11.11990        259.8076  4.477805e-85  335.5882
## 879         11.69526        346.4102  3.008950e-14  340.5000
## 1027        12.14154        433.0127  1.482049e-05  382.5000
## 1036        10.14057        159.2168  1.000000e+00  253.0000
##      resY.mean.log1p resY.mean.nexp resY.mean.root2 resY.min
## 2           6.057824  6.273804e-186        20.65053      361
## 254         5.779302  8.424674e-141        17.95921      145
## 662         5.818860  1.802525e-146        18.31907      225
## 879         5.833348  1.326566e-148        18.45264      281
## 1027        5.949340  7.627122e-167        19.55761      375
## 1036        5.537334  1.328912e-110        15.90597      129
##      resY.min.log1p resY.min.nexp resY.min.root2
## 2          5.891644 1.658410e-157       19.00000
## 254        4.983607  1.064879e-63       12.04159
## 662        5.420535  1.921948e-98       15.00000
## 879        5.641907 9.188626e-123       16.76305
## 1027       5.929589 1.379016e-163       19.36492
## 1036       4.867534  9.462629e-57       11.35782
##      business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 254         1402                              Y  254  329.3333
## 1027        2846                              Y 1027  500.0000
## 14          1026                              Y   14  428.2785
## 1383        3521                              Y 1383  409.6552
## 1630        3946                              Y 1630  419.5556
## 6            101                              Y    6  433.4380
##      resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min.nexp
## 254         5.800102  9.383193e-144        18.14754  3.342796e-80
## 1027        6.216606  7.124576e-218        22.36068 7.124576e-218
## 14          6.062106  1.002349e-186        20.69489 9.188626e-123
## 1383        6.017754  1.227493e-178        20.23994  2.937482e-30
## 1630        6.041577  6.156549e-183        20.48306 4.817492e-144
## 6           6.074053  5.757861e-189        20.81917 1.399426e-130
##      resXY.mean resXY.mean.log1p resXY.mean.root2 resXY.min
## 254    115110.0         11.65365         339.2786     27985
## 1027   191250.0         12.16134         437.3214    187500
## 14     180094.9         12.10124         424.3759    131000
## 1383   180536.4         12.10369         424.8958      4556
## 1630   202740.7         12.21969         450.2674    165000
## 6      184607.1         12.12599         429.6593    124848
##      resXY.min.log1p resXY.min.root2 resY.mad.nexp resY.mean
## 254         10.23946       167.28718  3.857670e-36  322.5333
## 1027        12.14154       433.01270  1.482049e-05  382.5000
## 14          11.78296       361.93922  1.000000e+00  431.9114
## 1383         8.42442        67.49815  1.000000e+00  438.5172
## 1630        12.01371       406.20192  1.000000e+00  485.9259
## 6           11.73486       353.33836  1.000000e+00  435.3471
##      resY.mean.log1p resY.mean.nexp resY.mean.root2 resY.min
## 254         5.779302  8.424674e-141        17.95921      145
## 1027        5.949340  7.627122e-167        19.55761      375
## 14          6.070533  2.650120e-188        20.78248      262
## 1383        6.085677  3.584114e-191        20.94080       67
## 1630        6.188112  9.226814e-212        22.04373      370
## 6           6.078438  8.534017e-190        20.86497      282
##      resY.min.log1p resY.min.nexp resY.min.root2
## 254        4.983607  1.064879e-63      12.041595
## 1027       5.929589 1.379016e-163      19.364917
## 14         5.572154 1.640007e-114      16.186414
## 1383       4.219508  7.984904e-30       8.185353
## 1630       5.916202 2.046641e-161      19.235384
## 6          5.645447 3.380307e-123      16.792856
##     business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 7          1011                              Y    7  412.1000
## 13         1024                              Y   13  462.6364
## 586        2007                              Y  586  401.5652
## 6           101                              Y    6  433.4380
## 12         1022                              Y   12  453.3919
## 748        2315                              Y  748  437.1244
##     resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min.nexp
## 7          6.023690  1.064741e-179        20.30025 2.162867e-105
## 13         6.139101  1.201103e-201        21.50898 6.519766e-145
## 586        5.997857  4.003520e-175        20.03909  1.404379e-54
## 6          6.074053  5.757861e-189        20.81917 1.399426e-130
## 12         6.118960  1.242824e-197        21.29300 9.188626e-123
## 748        6.082503  1.442998e-190        20.90752  2.678637e-33
##     resXY.mean resXY.mean.log1p resXY.mean.root2 resXY.min resXY.min.log1p
## 7     179669.2         12.09888         423.8740     87001        11.37369
## 13    183931.8         12.12233         428.8727    166000        12.01975
## 586   180321.6         12.10250         424.6429      7065         8.86305
## 6     184607.1         12.12599         429.6593    124848        11.73486
## 12    184123.6         12.12337         429.0963     76800        11.24897
## 748   180895.3         12.10568         425.3179      3675         8.20958
##     resXY.min.root2 resY.mad.nexp resY.mean resY.mean.log1p resY.mean.nexp
## 7         294.95932  1.000000e+00  445.6286        6.101727  2.923953e-194
## 13        407.43098  9.053782e-28  405.2273        6.006913  1.028091e-176
## 586        84.05355  1.000000e+00  447.9565        6.106926  2.850719e-195
## 6         353.33836  1.000000e+00  435.3471        6.078438  8.534017e-190
## 12        277.12813  1.160754e-49  414.1229        6.028575  1.408288e-180
## 748        60.62178  6.775247e-62  419.7319        6.041996  5.161464e-183
##     resY.mean.root2 resY.min resY.min.log1p resY.min.nexp resY.min.root2
## 7          21.10992      274       5.616771 1.007655e-119      16.552945
## 13         20.13026      332       5.808142 6.519766e-145      18.220867
## 586        21.16498       45       3.828641  2.862519e-20       6.708204
## 6          20.86497      282       5.645447 3.380307e-123      16.792856
## 12         20.35001      240       5.484797 5.879283e-105      15.491933
## 748        20.48736       49       3.912023  5.242886e-22       7.000000
##                                id        cor.y exclude.as.feat   cor.y.abs
## .pos                         .pos  0.027497300           FALSE 0.027497300
## resX.mean               resX.mean -0.017726551           FALSE 0.017726551
## resX.mean.log1p   resX.mean.log1p -0.015059015           FALSE 0.015059015
## resX.mean.nexp     resX.mean.nexp -0.022433472           FALSE 0.022433472
## resX.mean.root2   resX.mean.root2 -0.016434019           FALSE 0.016434019
## resX.min.nexp       resX.min.nexp -0.022391602           FALSE 0.022391602
## resXY.mean             resXY.mean -0.009002880           FALSE 0.009002880
## resXY.mean.log1p resXY.mean.log1p -0.004867571           FALSE 0.004867571
## resXY.mean.root2 resXY.mean.root2 -0.007039955           FALSE 0.007039955
## resXY.min               resXY.min -0.049458217           FALSE 0.049458217
## resXY.min.log1p   resXY.min.log1p -0.033756424           FALSE 0.033756424
## resXY.min.root2   resXY.min.root2 -0.041449898           FALSE 0.041449898
## resY.mad.nexp       resY.mad.nexp  0.012190340           FALSE 0.012190340
## resY.mean               resY.mean  0.012599188           FALSE 0.012599188
## resY.mean.log1p   resY.mean.log1p  0.013625190           FALSE 0.013625190
## resY.mean.nexp     resY.mean.nexp -0.022433472           FALSE 0.022433472
## resY.mean.root2   resY.mean.root2  0.013106506           FALSE 0.013106506
## resY.min                 resY.min -0.050925308           FALSE 0.050925308
## resY.min.log1p     resY.min.log1p -0.043072548           FALSE 0.043072548
## resY.min.nexp       resY.min.nexp -0.022433600           FALSE 0.022433600
## resY.min.root2     resY.min.root2 -0.047387777           FALSE 0.047387777
##                       cor.high.X freqRatio percentUnique zeroVar   nzv
## .pos                        <NA>  1.000000        100.00   FALSE FALSE
## resX.mean                   <NA>  2.000000         97.75   FALSE FALSE
## resX.mean.log1p        resX.mean  2.000000         97.60   FALSE FALSE
## resX.mean.nexp              <NA>  2.000000         97.75   FALSE FALSE
## resX.mean.root2        resX.mean  2.000000         97.45   FALSE FALSE
## resX.min.nexp               <NA>  6.000000         11.45   FALSE FALSE
## resXY.mean                  <NA>  6.000000         98.55   FALSE FALSE
## resXY.mean.log1p            <NA>  4.000000         90.80   FALSE FALSE
## resXY.mean.root2            <NA>  6.000000         98.20   FALSE FALSE
## resXY.min               resY.min  9.745455         37.65   FALSE FALSE
## resXY.min.log1p        resXY.min  9.745455         37.65   FALSE FALSE
## resXY.min.root2        resXY.min  9.745455         37.65   FALSE FALSE
## resY.mad.nexp               <NA>  5.354497          9.05   FALSE FALSE
## resY.mean        resY.mean.root2  1.666667         98.15   FALSE FALSE
## resY.mean.log1p             <NA>  1.666667         97.90   FALSE FALSE
## resY.mean.nexp    resX.mean.nexp  1.666667         98.15   FALSE FALSE
## resY.mean.root2  resY.mean.log1p  1.666667         97.85   FALSE FALSE
## resY.min                    <NA>  9.824561         13.85   FALSE FALSE
## resY.min.log1p          resY.min  9.824561         13.85   FALSE FALSE
## resY.min.nexp               <NA>  9.824561         13.85   FALSE FALSE
## resY.min.root2          resY.min  9.824561         13.85   FALSE FALSE
##                  is.cor.y.abs.low interaction.feat shapiro.test.p.value
## .pos                        FALSE               NA         2.145811e-24
## resX.mean                   FALSE               NA         1.161337e-19
## resX.mean.log1p             FALSE               NA         2.973500e-25
## resX.mean.nexp              FALSE               NA         1.194234e-72
## resX.mean.root2             FALSE               NA         1.959497e-22
## resX.min.nexp               FALSE               NA         1.195403e-72
## resXY.mean                  FALSE               NA         2.964553e-36
## resXY.mean.log1p             TRUE               NA         6.980019e-43
## resXY.mean.root2             TRUE               NA         1.780045e-39
## resXY.min                   FALSE               NA         2.084930e-32
## resXY.min.log1p             FALSE               NA         1.076069e-42
## resXY.min.root2             FALSE               NA         1.752753e-35
## resY.mad.nexp               FALSE               NA         1.839563e-53
## resY.mean                   FALSE               NA         1.464051e-21
## resY.mean.log1p             FALSE               NA         3.854130e-28
## resY.mean.nexp              FALSE               NA         1.194234e-72
## resY.mean.root2             FALSE               NA         7.216658e-25
## resY.min                    FALSE               NA         1.973528e-28
## resY.min.log1p              FALSE               NA         3.088017e-38
## resY.min.nexp               FALSE               NA         1.194238e-72
## resY.min.root2              FALSE               NA         2.137435e-32
##                  rsp_var_raw id_var rsp_var           max           min
## .pos                   FALSE     NA      NA  1.200000e+04  1.000000e+00
## resX.mean              FALSE     NA      NA  5.000000e+02  2.837692e+02
## resX.mean.log1p        FALSE     NA      NA  6.216606e+00  5.651679e+00
## resX.mean.nexp         FALSE     NA      NA 5.762208e-124 7.124576e-218
## resX.mean.root2        FALSE     NA      NA  2.236068e+01  1.684545e+01
## resX.min.nexp          FALSE     NA      NA  9.602680e-24 7.124576e-218
## resXY.mean             FALSE     NA      NA  2.500000e+05  8.762615e+04
## resXY.mean.log1p       FALSE     NA      NA  1.242922e+01  1.138085e+01
## resXY.mean.root2       FALSE     NA      NA  5.000000e+02  2.960172e+02
## resXY.min              FALSE     NA      NA  2.500000e+05  1.802000e+03
## resXY.min.log1p        FALSE     NA      NA  1.242922e+01  7.497207e+00
## resXY.min.root2        FALSE     NA      NA  5.000000e+02  4.244997e+01
## resY.mad.nexp          FALSE     NA      NA  1.000000e+00 8.904719e-122
## resY.mean              FALSE     NA      NA  5.000000e+02  2.530000e+02
## resY.mean.log1p        FALSE     NA      NA  6.216606e+00  5.537334e+00
## resY.mean.nexp         FALSE     NA      NA 1.328912e-110 7.124576e-218
## resY.mean.root2        FALSE     NA      NA  2.236068e+01  1.590597e+01
## resY.min               FALSE     NA      NA  5.000000e+02  2.900000e+01
## resY.min.log1p         FALSE     NA      NA  6.216606e+00  3.401197e+00
## resY.min.nexp          FALSE     NA      NA  2.543666e-13 7.124576e-218
## resY.min.root2         FALSE     NA      NA  2.236068e+01  5.385165e+00
##                  max.outdoor.fctr.N max.outdoor.fctr.Y min.outdoor.fctr.N
## .pos                   2.000000e+03       1.996000e+03       3.000000e+00
## resX.mean              4.910714e+02       4.940000e+02       3.553137e+02
## resX.mean.log1p        6.198624e+00       6.204558e+00       5.875812e+00
## resX.mean.nexp        4.888883e-155      1.209672e-151      5.375122e-214
## resX.mean.root2        2.216013e+01       2.222611e+01       1.884977e+01
## resX.min.nexp          3.221340e-27       2.937482e-30      1.379016e-163
## resXY.mean             2.087027e+05       2.011129e+05       1.348537e+05
## resXY.mean.log1p       1.224867e+01       1.221163e+01       1.181195e+01
## resXY.mean.root2       4.568399e+02       4.484562e+02       3.672243e+02
## resXY.min              1.875000e+05       1.875000e+05       5.000000e+03
## resXY.min.log1p        1.214154e+01       1.214154e+01       8.517393e+00
## resXY.min.root2        4.330127e+02       4.330127e+02       7.071068e+01
## resY.mad.nexp          1.000000e+00       1.000000e+00       3.268701e-81
## resY.mean              4.847273e+02       4.851765e+02       3.266000e+02
## resY.mean.log1p        6.185647e+00       6.186572e+00       5.791793e+00
## resY.mean.nexp        1.443518e-142      2.530221e-127      3.059287e-211
## resY.mean.root2        2.201652e+01       2.202672e+01       1.807208e+01
## resY.min               3.750000e+02       3.750000e+02       5.000000e+01
## resY.min.log1p         5.929589e+00       5.929589e+00       3.931826e+00
## resY.min.nexp          1.928750e-22       7.095474e-23      1.379016e-163
## resY.min.root2         1.936492e+01       1.936492e+01       7.071068e+00
##                  min.outdoor.fctr.Y max.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                   1.500000e+01                         1.999000e+03
## resX.mean              3.475000e+02                         5.000000e+02
## resX.mean.log1p        5.853638e+00                         6.216606e+00
## resX.mean.nexp        2.874259e-215                        5.762208e-124
## resX.mean.root2        1.864135e+01                         2.236068e+01
## resX.min.nexp         1.379016e-163                         2.937482e-30
## resXY.mean             1.137250e+05                         2.182778e+05
## resXY.mean.log1p       1.164155e+01                         1.229353e+01
## resXY.mean.root2       3.372314e+02                         4.672021e+02
## resXY.min              4.624000e+03                         1.875000e+05
## resXY.min.log1p        8.439232e+00                         1.214154e+01
## resXY.min.root2        6.800000e+01                         4.330127e+02
## resY.mad.nexp          3.268701e-81                         1.000000e+00
## resY.mean              2.915000e+02                         5.000000e+02
## resY.mean.log1p        5.678465e+00                         6.216606e+00
## resY.mean.nexp        1.952253e-211                        1.328912e-110
## resY.mean.root2        1.707337e+01                         2.236068e+01
## resY.min               5.100000e+01                         5.000000e+02
## resY.min.log1p         3.951244e+00                         6.216606e+00
## resY.min.nexp         1.379016e-163                         2.543666e-13
## resY.min.root2         7.141428e+00                         2.236068e+01
##                  min.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                                     1.000000e+00
## resX.mean                                2.837692e+02
## resX.mean.log1p                          5.651679e+00
## resX.mean.nexp                          7.124576e-218
## resX.mean.root2                          1.684545e+01
## resX.min.nexp                           7.124576e-218
## resXY.mean                               8.762615e+04
## resXY.mean.log1p                         1.138085e+01
## resXY.mean.root2                         2.960172e+02
## resXY.min                                3.675000e+03
## resXY.min.log1p                          8.209580e+00
## resXY.min.root2                          6.062178e+01
## resY.mad.nexp                            4.477805e-85
## resY.mean                                2.530000e+02
## resY.mean.log1p                          5.537334e+00
## resY.mean.nexp                          7.124576e-218
## resY.mean.root2                          1.590597e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.nexp                           7.124576e-218
## resY.min.root2                           5.385165e+00
##                  max.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     1.200000e+04
## resX.mean                                5.000000e+02
## resX.mean.log1p                          6.216606e+00
## resX.mean.nexp                          5.719134e-133
## resX.mean.root2                          2.236068e+01
## resX.min.nexp                            9.602680e-24
## resXY.mean                               2.500000e+05
## resXY.mean.log1p                         1.242922e+01
## resXY.mean.root2                         5.000000e+02
## resXY.min                                2.500000e+05
## resXY.min.log1p                          1.242922e+01
## resXY.min.root2                          5.000000e+02
## resY.mad.nexp                            1.000000e+00
## resY.mean                                5.000000e+02
## resY.mean.log1p                          6.216606e+00
## resY.mean.nexp                          1.105028e-116
## resY.mean.root2                          2.236068e+01
## resY.min                                 5.000000e+02
## resY.min.log1p                           6.216606e+00
## resY.min.nexp                            2.543666e-13
## resY.min.root2                           2.236068e+01
##                  min.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     2.001000e+03
## resX.mean                                3.045000e+02
## resX.mean.log1p                          5.721950e+00
## resX.mean.nexp                          7.124576e-218
## resX.mean.root2                          1.744993e+01
## resX.min.nexp                           7.124576e-218
## resXY.mean                               1.058460e+05
## resXY.mean.log1p                         1.156975e+01
## resXY.mean.root2                         3.253398e+02
## resXY.min                                1.802000e+03
## resXY.min.log1p                          7.497207e+00
## resXY.min.root2                          4.244997e+01
## resY.mad.nexp                           8.904719e-122
## resY.mean                                2.670000e+02
## resY.mean.log1p                          5.590987e+00
## resY.mean.nexp                          7.124576e-218
## resY.mean.root2                          1.634013e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.nexp                           7.124576e-218
## resY.min.root2                           5.385165e+00
## [1] "OOBobs outdoor.fctr.All.X..rcv.glmnet Y: max > max of Train range: 28"
##      business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 254         1402                              Y  254  329.3333
## 662         2146                              Y  662  414.7059
## 879         2562                              Y  879  492.8571
## 962         2719                              Y  962  446.1111
## 1027        2846                              Y 1027  500.0000
## 1036         286                              Y 1036  283.7692
##      resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min
## 254         5.800102  9.383193e-144        18.14754      183
## 662         6.029978  7.861831e-181        20.36433      300
## 879         6.202246  9.012857e-215        22.20039      400
## 962         6.102807  1.804705e-194        21.12134      282
## 1027        6.216606  7.124576e-218        22.36068      500
## 1036        5.651679  5.762208e-124        16.84545      195
##      resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 254        5.214936       13.52775  115110.00         11.65365
## 662        5.707110       17.32051  144558.82         11.88145
## 879        5.993961       20.00000  168107.14         12.03236
## 962        5.645447       16.79286  201222.22         12.21217
## 1027       6.216606       22.36068  191250.00         12.16134
## 1036       5.278115       13.96424   87626.15         11.38085
##      resXY.mean.root2 resY.mad resY.mad.log1p resY.mad.root2 resY.mean
## 254          339.2786  81.5430       4.413319       9.030116  322.5333
## 662          380.2089 194.2206       5.274130      13.936305  335.5882
## 879          410.0087  31.1346       3.469933       5.579839  340.5000
## 962          448.5780   0.0000       0.000000       0.000000  456.3333
## 1027         437.3214  11.1195       2.494816       3.334591  382.5000
## 1036         296.0172   0.0000       0.000000       0.000000  253.0000
##      resY.mean.log1p resY.mean.nexp resY.mean.root2 resY.min
## 254         5.779302  8.424674e-141        17.95921      145
## 662         5.818860  1.802525e-146        18.31907      225
## 879         5.833348  1.326566e-148        18.45264      281
## 962         6.125413  6.560719e-199        21.36196      373
## 1027        5.949340  7.627122e-167        19.55761      375
## 1036        5.537334  1.328912e-110        15.90597      129
##      resY.min.log1p resY.min.nexp resY.min.root2
## 254        4.983607  1.064879e-63       12.04159
## 662        5.420535  1.921948e-98       15.00000
## 879        5.641907 9.188626e-123       16.76305
## 962        5.924256 1.018963e-162       19.31321
## 1027       5.929589 1.379016e-163       19.36492
## 1036       4.867534  9.462629e-57       11.35782
##      business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 879         2562                              Y  879  492.8571
## 875         2557                              Y  875  453.4444
## 849         2495                              Y  849  472.4044
## 1999         998                              Y 1999  442.0125
## 43          1069                              Y   43  441.0667
## 1165         310                              Y 1165  500.0000
##      resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min
## 879         6.202246  9.012857e-215        22.20039      400
## 875         6.119076  1.179180e-197        21.29424      281
## 849         6.159950  6.876552e-206        21.73487      281
## 1999        6.093598  1.087453e-192        21.02409      281
## 43          6.091461  2.800145e-192        21.00159      373
## 1165        6.216606  7.124576e-218        22.36068      500
##      resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 879        5.993961       20.00000   168107.1         12.03236
## 875        5.641907       16.76305   202777.8         12.21987
## 849        5.641907       16.76305   217574.0         12.29030
## 1999       5.641907       16.76305   183271.9         12.11873
## 43         5.924256       19.31321   203733.3         12.22457
## 1165       6.216606       22.36068   190333.3         12.15654
##      resXY.mean.root2 resY.mad resY.mad.log1p resY.mad.root2 resY.mean
## 879          410.0087  31.1346       3.469933       5.579839  340.5000
## 875          450.3085   0.0000       0.000000       0.000000  452.1111
## 849          466.4483   0.0000       0.000000       0.000000  462.6838
## 1999         428.1026 139.3644       4.944242      11.805270  424.5312
## 43           451.3683   0.0000       0.000000       0.000000  466.4000
## 1165         436.2721   0.0000       0.000000       0.000000  380.6667
##      resY.mean.log1p resY.mean.nexp resY.mean.root2 resY.min
## 879         5.833348  1.326566e-148        18.45264      281
## 875         6.116137  4.473415e-197        21.26290      281
## 849         6.139203  1.145430e-201        21.51009      259
## 1999        6.053338  4.250311e-185        20.60416      279
## 43          6.147185  2.786465e-203        21.59630      373
## 1165        5.944548  4.770536e-166        19.51068      373
##      resY.min.log1p resY.min.nexp resY.min.root2
## 879        5.641907 9.188626e-123       16.76305
## 875        5.641907 9.188626e-123       16.76305
## 849        5.560682 3.294042e-113       16.09348
## 1999       5.634790 6.789527e-122       16.70329
## 43         5.924256 1.018963e-162       19.31321
## 1165       5.924256 1.018963e-162       19.31321
##      business_id outdoor.fctr.All.X..rcv.glmnet .pos resX.mean
## 1054        2895                              Y 1054  415.3617
## 1997         993                              Y 1997  428.4412
## 553         1944                              Y  553  452.2698
## 586         2007                              Y  586  401.5652
## 599         2031                              Y  599  448.7042
## 748         2315                              Y  748  437.1244
##      resX.mean.log1p resX.mean.nexp resX.mean.root2 resX.min
## 1054        6.031554  4.080419e-181        20.38042      119
## 1997        6.062485  8.518463e-187        20.69882      208
## 553         6.116488  3.816837e-197        21.26664      281
## 586         5.997857  4.003520e-175        20.03909      124
## 599         6.108590  1.349680e-195        21.18264      281
## 748         6.082503  1.442998e-190        20.90752       75
##      resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 1054       4.787492      10.908712   189951.8         12.15453
## 1997       5.342334      14.422205   172134.1         12.05604
## 553        5.641907      16.763055   201468.3         12.21339
## 586        4.828314      11.135529   180321.6         12.10250
## 599        5.641907      16.763055   202484.5         12.21842
## 748        4.330733       8.660254   180895.3         12.10568
##      resXY.mean.root2 resY.mad resY.mad.log1p resY.mad.root2 resY.mean
## 1054         435.8346   0.0000       0.000000        0.00000  457.1489
## 1997         414.8905 140.1057       4.949509       11.83663  412.7353
## 553          448.8522   0.0000       0.000000        0.00000  450.6667
## 586          424.6429   0.0000       0.000000        0.00000  447.9565
## 599          449.9827   0.0000       0.000000        0.00000  456.1268
## 748          425.3179 140.8470       4.954749       11.86790  419.7319
##      resY.mean.log1p resY.mean.nexp resY.mean.root2 resY.min
## 1054        6.127194  2.902282e-199        21.38104       44
## 1997        6.025226  5.640782e-180        20.31589      280
## 553         6.112944  1.896503e-196        21.22891      281
## 586         6.106926  2.850719e-195        21.16498       45
## 599         6.124961  8.066123e-199        21.35712      281
## 748         6.041996  5.161464e-183        20.48736       49
##      resY.min.log1p resY.min.nexp resY.min.root2
## 1054       3.806662  7.781132e-20       6.633250
## 1997       5.638355 2.497728e-122      16.733201
## 553        5.641907 9.188626e-123      16.763055
## 586        3.828641  2.862519e-20       6.708204
## 599        5.641907 9.188626e-123      16.763055
## 748        3.912023  5.242886e-22       7.000000
##                                id        cor.y exclude.as.feat   cor.y.abs
## .pos                         .pos  0.027497300           FALSE 0.027497300
## resX.mean               resX.mean -0.017726551           FALSE 0.017726551
## resX.mean.log1p   resX.mean.log1p -0.015059015           FALSE 0.015059015
## resX.mean.nexp     resX.mean.nexp -0.022433472           FALSE 0.022433472
## resX.mean.root2   resX.mean.root2 -0.016434019           FALSE 0.016434019
## resX.min                 resX.min -0.031436275           FALSE 0.031436275
## resX.min.log1p     resX.min.log1p -0.030103276           FALSE 0.030103276
## resX.min.root2     resX.min.root2 -0.030339745           FALSE 0.030339745
## resXY.mean             resXY.mean -0.009002880           FALSE 0.009002880
## resXY.mean.log1p resXY.mean.log1p -0.004867571           FALSE 0.004867571
## resXY.mean.root2 resXY.mean.root2 -0.007039955           FALSE 0.007039955
## resY.mad                 resY.mad  0.007630633           FALSE 0.007630633
## resY.mad.log1p     resY.mad.log1p -0.001526058           FALSE 0.001526058
## resY.mad.root2     resY.mad.root2  0.002557583           FALSE 0.002557583
## resY.mean               resY.mean  0.012599188           FALSE 0.012599188
## resY.mean.log1p   resY.mean.log1p  0.013625190           FALSE 0.013625190
## resY.mean.nexp     resY.mean.nexp -0.022433472           FALSE 0.022433472
## resY.mean.root2   resY.mean.root2  0.013106506           FALSE 0.013106506
## resY.min                 resY.min -0.050925308           FALSE 0.050925308
## resY.min.log1p     resY.min.log1p -0.043072548           FALSE 0.043072548
## resY.min.nexp       resY.min.nexp -0.022433600           FALSE 0.022433600
## resY.min.root2     resY.min.root2 -0.047387777           FALSE 0.047387777
##                       cor.high.X freqRatio percentUnique zeroVar   nzv
## .pos                        <NA>  1.000000        100.00   FALSE FALSE
## resX.mean                   <NA>  2.000000         97.75   FALSE FALSE
## resX.mean.log1p        resX.mean  2.000000         97.60   FALSE FALSE
## resX.mean.nexp              <NA>  2.000000         97.75   FALSE FALSE
## resX.mean.root2        resX.mean  2.000000         97.45   FALSE FALSE
## resX.min                    <NA>  6.000000         11.45   FALSE FALSE
## resX.min.log1p          resX.min  6.000000         11.45   FALSE FALSE
## resX.min.root2          resX.min  6.000000         11.45   FALSE FALSE
## resXY.mean                  <NA>  6.000000         98.55   FALSE FALSE
## resXY.mean.log1p            <NA>  4.000000         90.80   FALSE FALSE
## resXY.mean.root2            <NA>  6.000000         98.20   FALSE FALSE
## resY.mad                    <NA>  5.354497          9.05   FALSE FALSE
## resY.mad.log1p              <NA>  5.354497          9.05   FALSE FALSE
## resY.mad.root2              <NA>  5.354497          9.05   FALSE FALSE
## resY.mean        resY.mean.root2  1.666667         98.15   FALSE FALSE
## resY.mean.log1p             <NA>  1.666667         97.90   FALSE FALSE
## resY.mean.nexp    resX.mean.nexp  1.666667         98.15   FALSE FALSE
## resY.mean.root2  resY.mean.log1p  1.666667         97.85   FALSE FALSE
## resY.min                    <NA>  9.824561         13.85   FALSE FALSE
## resY.min.log1p          resY.min  9.824561         13.85   FALSE FALSE
## resY.min.nexp               <NA>  9.824561         13.85   FALSE FALSE
## resY.min.root2          resY.min  9.824561         13.85   FALSE FALSE
##                  is.cor.y.abs.low interaction.feat shapiro.test.p.value
## .pos                        FALSE               NA         2.145811e-24
## resX.mean                   FALSE               NA         1.161337e-19
## resX.mean.log1p             FALSE               NA         2.973500e-25
## resX.mean.nexp              FALSE               NA         1.194234e-72
## resX.mean.root2             FALSE               NA         1.959497e-22
## resX.min                    FALSE               NA         2.978480e-35
## resX.min.log1p              FALSE               NA         1.543786e-43
## resX.min.root2              FALSE               NA         7.580680e-39
## resXY.mean                  FALSE               NA         2.964553e-36
## resXY.mean.log1p             TRUE               NA         6.980019e-43
## resXY.mean.root2             TRUE               NA         1.780045e-39
## resY.mad                     TRUE               NA         3.711302e-48
## resY.mad.log1p               TRUE               NA         3.133148e-49
## resY.mad.root2               TRUE               NA         1.717662e-47
## resY.mean                   FALSE               NA         1.464051e-21
## resY.mean.log1p             FALSE               NA         3.854130e-28
## resY.mean.nexp              FALSE               NA         1.194234e-72
## resY.mean.root2             FALSE               NA         7.216658e-25
## resY.min                    FALSE               NA         1.973528e-28
## resY.min.log1p              FALSE               NA         3.088017e-38
## resY.min.nexp               FALSE               NA         1.194238e-72
## resY.min.root2              FALSE               NA         2.137435e-32
##                  rsp_var_raw id_var rsp_var           max           min
## .pos                   FALSE     NA      NA  1.200000e+04  1.000000e+00
## resX.mean              FALSE     NA      NA  5.000000e+02  2.837692e+02
## resX.mean.log1p        FALSE     NA      NA  6.216606e+00  5.651679e+00
## resX.mean.nexp         FALSE     NA      NA 5.762208e-124 7.124576e-218
## resX.mean.root2        FALSE     NA      NA  2.236068e+01  1.684545e+01
## resX.min               FALSE     NA      NA  5.000000e+02  5.300000e+01
## resX.min.log1p         FALSE     NA      NA  6.216606e+00  3.988984e+00
## resX.min.root2         FALSE     NA      NA  2.236068e+01  7.280110e+00
## resXY.mean             FALSE     NA      NA  2.500000e+05  8.762615e+04
## resXY.mean.log1p       FALSE     NA      NA  1.242922e+01  1.138085e+01
## resXY.mean.root2       FALSE     NA      NA  5.000000e+02  2.960172e+02
## resY.mad               FALSE     NA      NA  2.787288e+02  0.000000e+00
## resY.mad.log1p         FALSE     NA      NA  5.633821e+00  0.000000e+00
## resY.mad.root2         FALSE     NA      NA  1.669517e+01  0.000000e+00
## resY.mean              FALSE     NA      NA  5.000000e+02  2.530000e+02
## resY.mean.log1p        FALSE     NA      NA  6.216606e+00  5.537334e+00
## resY.mean.nexp         FALSE     NA      NA 1.328912e-110 7.124576e-218
## resY.mean.root2        FALSE     NA      NA  2.236068e+01  1.590597e+01
## resY.min               FALSE     NA      NA  5.000000e+02  2.900000e+01
## resY.min.log1p         FALSE     NA      NA  6.216606e+00  3.401197e+00
## resY.min.nexp          FALSE     NA      NA  2.543666e-13 7.124576e-218
## resY.min.root2         FALSE     NA      NA  2.236068e+01  5.385165e+00
##                  max.outdoor.fctr.N max.outdoor.fctr.Y min.outdoor.fctr.N
## .pos                   2.000000e+03       1.996000e+03       3.000000e+00
## resX.mean              4.910714e+02       4.940000e+02       3.553137e+02
## resX.mean.log1p        6.198624e+00       6.204558e+00       5.875812e+00
## resX.mean.nexp        4.888883e-155      1.209672e-151      5.375122e-214
## resX.mean.root2        2.216013e+01       2.222611e+01       1.884977e+01
## resX.min               3.750000e+02       3.750000e+02       6.100000e+01
## resX.min.log1p         5.929589e+00       5.929589e+00       4.127134e+00
## resX.min.root2         1.936492e+01       1.936492e+01       7.810250e+00
## resXY.mean             2.087027e+05       2.011129e+05       1.348537e+05
## resXY.mean.log1p       1.224867e+01       1.221163e+01       1.181195e+01
## resXY.mean.root2       4.568399e+02       4.484562e+02       3.672243e+02
## resY.mad               1.853250e+02       1.853250e+02       0.000000e+00
## resY.mad.log1p         5.227492e+00       5.227492e+00       0.000000e+00
## resY.mad.root2         1.361341e+01       1.361341e+01       0.000000e+00
## resY.mean              4.847273e+02       4.851765e+02       3.266000e+02
## resY.mean.log1p        6.185647e+00       6.186572e+00       5.791793e+00
## resY.mean.nexp        1.443518e-142      2.530221e-127      3.059287e-211
## resY.mean.root2        2.201652e+01       2.202672e+01       1.807208e+01
## resY.min               3.750000e+02       3.750000e+02       5.000000e+01
## resY.min.log1p         5.929589e+00       5.929589e+00       3.931826e+00
## resY.min.nexp          1.928750e-22       7.095474e-23      1.379016e-163
## resY.min.root2         1.936492e+01       1.936492e+01       7.071068e+00
##                  min.outdoor.fctr.Y max.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                   1.500000e+01                         1.999000e+03
## resX.mean              3.475000e+02                         5.000000e+02
## resX.mean.log1p        5.853638e+00                         6.216606e+00
## resX.mean.nexp        2.874259e-215                        5.762208e-124
## resX.mean.root2        1.864135e+01                         2.236068e+01
## resX.min               6.800000e+01                         5.000000e+02
## resX.min.log1p         4.234107e+00                         6.216606e+00
## resX.min.root2         8.246211e+00                         2.236068e+01
## resXY.mean             1.137250e+05                         2.182778e+05
## resXY.mean.log1p       1.164155e+01                         1.229353e+01
## resXY.mean.root2       3.372314e+02                         4.672021e+02
## resY.mad               0.000000e+00                         1.942206e+02
## resY.mad.log1p         0.000000e+00                         5.274130e+00
## resY.mad.root2         0.000000e+00                         1.393631e+01
## resY.mean              2.915000e+02                         5.000000e+02
## resY.mean.log1p        5.678465e+00                         6.216606e+00
## resY.mean.nexp        1.952253e-211                        1.328912e-110
## resY.mean.root2        1.707337e+01                         2.236068e+01
## resY.min               5.100000e+01                         5.000000e+02
## resY.min.log1p         3.951244e+00                         6.216606e+00
## resY.min.nexp         1.379016e-163                         2.543666e-13
## resY.min.root2         7.141428e+00                         2.236068e+01
##                  min.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                                     1.000000e+00
## resX.mean                                2.837692e+02
## resX.mean.log1p                          5.651679e+00
## resX.mean.nexp                          7.124576e-218
## resX.mean.root2                          1.684545e+01
## resX.min                                 6.800000e+01
## resX.min.log1p                           4.234107e+00
## resX.min.root2                           8.246211e+00
## resXY.mean                               8.762615e+04
## resXY.mean.log1p                         1.138085e+01
## resXY.mean.root2                         2.960172e+02
## resY.mad                                 0.000000e+00
## resY.mad.log1p                           0.000000e+00
## resY.mad.root2                           0.000000e+00
## resY.mean                                2.530000e+02
## resY.mean.log1p                          5.537334e+00
## resY.mean.nexp                          7.124576e-218
## resY.mean.root2                          1.590597e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.nexp                           7.124576e-218
## resY.min.root2                           5.385165e+00
##                  max.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     1.200000e+04
## resX.mean                                5.000000e+02
## resX.mean.log1p                          6.216606e+00
## resX.mean.nexp                          5.719134e-133
## resX.mean.root2                          2.236068e+01
## resX.min                                 5.000000e+02
## resX.min.log1p                           6.216606e+00
## resX.min.root2                           2.236068e+01
## resXY.mean                               2.500000e+05
## resXY.mean.log1p                         1.242922e+01
## resXY.mean.root2                         5.000000e+02
## resY.mad                                 2.787288e+02
## resY.mad.log1p                           5.633821e+00
## resY.mad.root2                           1.669517e+01
## resY.mean                                5.000000e+02
## resY.mean.log1p                          6.216606e+00
## resY.mean.nexp                          1.105028e-116
## resY.mean.root2                          2.236068e+01
## resY.min                                 5.000000e+02
## resY.min.log1p                           6.216606e+00
## resY.min.nexp                            2.543666e-13
## resY.min.root2                           2.236068e+01
##                  min.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     2.001000e+03
## resX.mean                                3.045000e+02
## resX.mean.log1p                          5.721950e+00
## resX.mean.nexp                          7.124576e-218
## resX.mean.root2                          1.744993e+01
## resX.min                                 5.300000e+01
## resX.min.log1p                           3.988984e+00
## resX.min.root2                           7.280110e+00
## resXY.mean                               1.058460e+05
## resXY.mean.log1p                         1.156975e+01
## resXY.mean.root2                         3.253398e+02
## resY.mad                                 0.000000e+00
## resY.mad.log1p                           0.000000e+00
## resY.mad.root2                           0.000000e+00
## resY.mean                                2.670000e+02
## resY.mean.log1p                          5.590987e+00
## resY.mean.nexp                          7.124576e-218
## resY.mean.root2                          1.634013e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.nexp                           7.124576e-218
## resY.min.root2                           5.385165e+00
## [1] "OOBobs total range outliers: 38"
## [1] "newobs outdoor.fctr.Final..rcv.glmnet Y: min < min of Train range: 43"
##      business_id outdoor.fctr.Final..rcv.glmnet nImgs nImgs.log1p
## 2222       0qj4g                              Y    12   2.5649494
## 2316       12p62                              Y     1   0.6931472
## 2363       18cak                              Y    15   2.7725887
## 2473       1lvgn                              Y     1   0.6931472
## 2821       2yeud                              Y    96   4.5747110
## 2850       31y16                              Y     6   1.9459101
##      nImgs.root2 resX.mean resX.mean.log1p resX.mean.root2 resX.min
## 2222    3.464102  382.1667        5.948470        19.54908       53
## 2316    1.000000  500.0000        6.216606        22.36068      500
## 2363    3.872983  343.2667        5.841417        18.52746      110
## 2473    1.000000  500.0000        6.216606        22.36068      500
## 2821    9.797959  451.9271        6.115731        21.25858      280
## 2850    2.449490  344.5000        5.844993        18.56071       96
##      resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 2222       3.988984       7.280110   171250.5         12.05089
## 2316       6.216606      22.360680   187500.0         12.14154
## 2363       4.709530      10.488088   122284.5         11.71411
## 2473       6.216606      22.360680   187500.0         12.14154
## 2821       5.638355      16.733201   183807.3         12.12165
## 2850       4.574711       9.797959   105846.0         11.56975
##      resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2
## 2222         413.8242      1802        7.497207        42.44997
## 2316         433.0127    187500       12.141539       433.01270
## 2363         349.6920     28160       10.245693       167.80942
## 2473         433.0127    187500       12.141539       433.01270
## 2821         428.7275     20500        9.928229       143.17821
## 2850         325.3398     12288        9.416460       110.85125
##      resY.mad.nexp resY.mean resY.mean.log1p resY.mean.root2 resY.min
## 2222  1.000000e+00  414.3333        6.029081        20.35518       34
## 2316  1.000000e+00  375.0000        5.929589        19.36492      375
## 2363  2.635452e-50  341.2000        5.835395        18.47160      198
## 2473  1.000000e+00  375.0000        5.929589        19.36492      375
## 2821  1.000000e+00  415.6875        6.032337        20.38842       41
## 2850  2.635452e-50  267.0000        5.590987        16.34013      128
##      resY.min.log1p resY.min.root2
## 2222       3.555348       5.830952
## 2316       5.929589      19.364917
## 2363       5.293305      14.071247
## 2473       5.929589      19.364917
## 2821       3.737670       6.403124
## 2850       4.859812      11.313708
##       business_id outdoor.fctr.Final..rcv.glmnet nImgs nImgs.log1p
## 2363        18cak                              Y    15    2.772589
## 3310        4q512                              Y     2    1.098612
## 5530        cijo0                              Y    34    3.555348
## 6364        fjvig                              Y   578    6.361302
## 9792        rx5mm                              Y   867    6.766192
## 11906       znmff                              Y    17    2.890372
##       nImgs.root2 resX.mean resX.mean.log1p resX.mean.root2 resX.min
## 2363     3.872983  343.2667        5.841417        18.52746      110
## 3310     1.414214  304.5000        5.721950        17.44993      234
## 5530     5.830952  426.2353        6.057335        20.64547      281
## 6364    24.041631  439.3478        6.087565        20.96062       63
## 9792    29.444864  439.4464        6.087789        20.96298       63
## 11906    4.123106  344.2941        5.844397        18.55516       97
##       resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 2363        4.709530      10.488088   122284.5         11.71411
## 3310        5.459586      15.297059   114225.0         11.64593
## 5530        5.641907      16.763055   174044.1         12.06707
## 6364        4.158883       7.937254   185841.0         12.13265
## 9792        4.158883       7.937254   185129.7         12.12882
## 11906       4.584967       9.848858   127450.6         11.75549
##       resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2
## 2363          349.6920     28160       10.245693       167.80942
## 3310          337.9719     40950       10.620132       202.36106
## 5530          417.1860     20500        9.928229       143.17821
## 6364          431.0928      9204        9.127502        95.93748
## 9792          430.2670     10836        9.290721       104.09611
## 11906         357.0022     12610        9.442325       112.29426
##       resY.mad.nexp resY.mean resY.mean.log1p resY.mean.root2 resY.min
## 2363   2.635452e-50  341.2000        5.835395        18.47160      198
## 3310  2.337156e-105  337.5000        5.824524        18.37117      175
## 5530   1.000000e+00  421.8529        6.047024        20.53906       41
## 6364   1.000000e+00  429.4844        6.064911        20.72401       78
## 9792   1.000000e+00  429.3633        6.064630        20.72108      156
## 11906  3.268701e-81  305.3529        5.724738        17.47435       97
##       resY.min.log1p resY.min.root2
## 2363        5.293305      14.071247
## 3310        5.170484      13.228757
## 5530        3.737670       6.403124
## 6364        4.369448       8.831761
## 9792        5.056246      12.489996
## 11906       4.584967       9.848858
##       business_id outdoor.fctr.Final..rcv.glmnet nImgs nImgs.log1p
## 10697       v7cb4                              Y   741    6.609349
## 11700       yww34                              Y   598    6.395262
## 11824       zedxo                              Y  1962    7.582229
## 11838       zfrmk                              Y    89    4.499810
## 11906       znmff                              Y    17    2.890372
## 11942       zsd8q                              Y    31    3.465736
##       nImgs.root2 resX.mean resX.mean.log1p resX.mean.root2 resX.min
## 10697   27.221315  442.8421        6.095469        21.04381       63
## 11700   24.454039  440.5217        6.090227        20.98861       67
## 11824   44.294469  439.5061        6.087924        20.96440       63
## 11838    9.433981  429.3708        6.064647        20.72126       63
## 11906    4.123106  344.2941        5.844397        18.55516       97
## 11942    5.567764  434.0323        6.075420        20.83344       63
##       resX.min.log1p resX.min.root2 resXY.mean resXY.mean.log1p
## 10697       4.158883       7.937254   186260.2         12.13491
## 11700       4.219508       8.185353   184358.9         12.12465
## 11824       4.158883       7.937254   183954.9         12.12245
## 11838       4.158883       7.937254   180279.1         12.10227
## 11906       4.584967       9.848858   127450.6         11.75549
## 11942       4.158883       7.937254   165092.0         12.01426
##       resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2
## 10697         431.5787     10836        9.290721       104.09611
## 11700         429.3704      6700        8.810012        81.85353
## 11824         428.8996     10836        9.290721       104.09611
## 11838         424.5928     10836        9.290721       104.09611
## 11906         357.0022     12610        9.442325       112.29426
## 11942         406.3152     10836        9.290721       104.09611
##       resY.mad.nexp resY.mean resY.mean.log1p resY.mean.root2 resY.min
## 10697  2.466466e-68  428.2740        6.062095        20.69478      160
## 11700  1.421895e-61  426.6338        6.058267        20.65512       77
## 11824  1.623407e-25  426.8282        6.058722        20.65982       84
## 11838  1.603375e-06  427.9663        6.061378        20.68735      172
## 11906  3.268701e-81  305.3529        5.724738        17.47435       97
## 11942  3.987632e-27  377.2258        5.935491        19.42230      172
##       resY.min.log1p resY.min.root2
## 10697       5.081404      12.649111
## 11700       4.356709       8.774964
## 11824       4.442651       9.165151
## 11838       5.153292      13.114877
## 11906       4.584967       9.848858
## 11942       5.153292      13.114877
##                                id        cor.y exclude.as.feat   cor.y.abs
## nImgs                       nImgs -0.014963676           FALSE 0.014963676
## nImgs.log1p           nImgs.log1p  0.047250893           FALSE 0.047250893
## nImgs.root2           nImgs.root2  0.014028124           FALSE 0.014028124
## resX.mean               resX.mean -0.017726551           FALSE 0.017726551
## resX.mean.log1p   resX.mean.log1p -0.015059015           FALSE 0.015059015
## resX.mean.root2   resX.mean.root2 -0.016434019           FALSE 0.016434019
## resX.min                 resX.min -0.031436275           FALSE 0.031436275
## resX.min.log1p     resX.min.log1p -0.030103276           FALSE 0.030103276
## resX.min.root2     resX.min.root2 -0.030339745           FALSE 0.030339745
## resXY.mean             resXY.mean -0.009002880           FALSE 0.009002880
## resXY.mean.log1p resXY.mean.log1p -0.004867571           FALSE 0.004867571
## resXY.mean.root2 resXY.mean.root2 -0.007039955           FALSE 0.007039955
## resXY.min               resXY.min -0.049458217           FALSE 0.049458217
## resXY.min.log1p   resXY.min.log1p -0.033756424           FALSE 0.033756424
## resXY.min.root2   resXY.min.root2 -0.041449898           FALSE 0.041449898
## resY.mad.nexp       resY.mad.nexp  0.012190340           FALSE 0.012190340
## resY.mean               resY.mean  0.012599188           FALSE 0.012599188
## resY.mean.log1p   resY.mean.log1p  0.013625190           FALSE 0.013625190
## resY.mean.root2   resY.mean.root2  0.013106506           FALSE 0.013106506
## resY.min                 resY.min -0.050925308           FALSE 0.050925308
## resY.min.log1p     resY.min.log1p -0.043072548           FALSE 0.043072548
## resY.min.root2     resY.min.root2 -0.047387777           FALSE 0.047387777
##                       cor.high.X freqRatio percentUnique zeroVar   nzv
## nImgs                       <NA>  1.033333         19.10   FALSE FALSE
## nImgs.log1p       nImgs.cut.fctr  1.033333         19.10   FALSE FALSE
## nImgs.root2          nImgs.log1p  1.033333         19.10   FALSE FALSE
## resX.mean                   <NA>  2.000000         97.75   FALSE FALSE
## resX.mean.log1p        resX.mean  2.000000         97.60   FALSE FALSE
## resX.mean.root2        resX.mean  2.000000         97.45   FALSE FALSE
## resX.min                    <NA>  6.000000         11.45   FALSE FALSE
## resX.min.log1p          resX.min  6.000000         11.45   FALSE FALSE
## resX.min.root2          resX.min  6.000000         11.45   FALSE FALSE
## resXY.mean                  <NA>  6.000000         98.55   FALSE FALSE
## resXY.mean.log1p            <NA>  4.000000         90.80   FALSE FALSE
## resXY.mean.root2            <NA>  6.000000         98.20   FALSE FALSE
## resXY.min               resY.min  9.745455         37.65   FALSE FALSE
## resXY.min.log1p        resXY.min  9.745455         37.65   FALSE FALSE
## resXY.min.root2        resXY.min  9.745455         37.65   FALSE FALSE
## resY.mad.nexp               <NA>  5.354497          9.05   FALSE FALSE
## resY.mean        resY.mean.root2  1.666667         98.15   FALSE FALSE
## resY.mean.log1p             <NA>  1.666667         97.90   FALSE FALSE
## resY.mean.root2  resY.mean.log1p  1.666667         97.85   FALSE FALSE
## resY.min                    <NA>  9.824561         13.85   FALSE FALSE
## resY.min.log1p          resY.min  9.824561         13.85   FALSE FALSE
## resY.min.root2          resY.min  9.824561         13.85   FALSE FALSE
##                  is.cor.y.abs.low interaction.feat shapiro.test.p.value
## nImgs                       FALSE               NA         1.364097e-61
## nImgs.log1p                 FALSE               NA         1.234907e-13
## nImgs.root2                 FALSE               NA         4.118632e-46
## resX.mean                   FALSE               NA         1.161337e-19
## resX.mean.log1p             FALSE               NA         2.973500e-25
## resX.mean.root2             FALSE               NA         1.959497e-22
## resX.min                    FALSE               NA         2.978480e-35
## resX.min.log1p              FALSE               NA         1.543786e-43
## resX.min.root2              FALSE               NA         7.580680e-39
## resXY.mean                  FALSE               NA         2.964553e-36
## resXY.mean.log1p             TRUE               NA         6.980019e-43
## resXY.mean.root2             TRUE               NA         1.780045e-39
## resXY.min                   FALSE               NA         2.084930e-32
## resXY.min.log1p             FALSE               NA         1.076069e-42
## resXY.min.root2             FALSE               NA         1.752753e-35
## resY.mad.nexp               FALSE               NA         1.839563e-53
## resY.mean                   FALSE               NA         1.464051e-21
## resY.mean.log1p             FALSE               NA         3.854130e-28
## resY.mean.root2             FALSE               NA         7.216658e-25
## resY.min                    FALSE               NA         1.973528e-28
## resY.min.log1p              FALSE               NA         3.088017e-38
## resY.min.root2              FALSE               NA         2.137435e-32
##                  rsp_var_raw id_var rsp_var          max           min
## nImgs                  FALSE     NA      NA 2.974000e+03  1.000000e+00
## nImgs.log1p            FALSE     NA      NA 7.997999e+00  6.931472e-01
## nImgs.root2            FALSE     NA      NA 5.453439e+01  1.000000e+00
## resX.mean              FALSE     NA      NA 5.000000e+02  2.837692e+02
## resX.mean.log1p        FALSE     NA      NA 6.216606e+00  5.651679e+00
## resX.mean.root2        FALSE     NA      NA 2.236068e+01  1.684545e+01
## resX.min               FALSE     NA      NA 5.000000e+02  5.300000e+01
## resX.min.log1p         FALSE     NA      NA 6.216606e+00  3.988984e+00
## resX.min.root2         FALSE     NA      NA 2.236068e+01  7.280110e+00
## resXY.mean             FALSE     NA      NA 2.500000e+05  8.762615e+04
## resXY.mean.log1p       FALSE     NA      NA 1.242922e+01  1.138085e+01
## resXY.mean.root2       FALSE     NA      NA 5.000000e+02  2.960172e+02
## resXY.min              FALSE     NA      NA 2.500000e+05  1.802000e+03
## resXY.min.log1p        FALSE     NA      NA 1.242922e+01  7.497207e+00
## resXY.min.root2        FALSE     NA      NA 5.000000e+02  4.244997e+01
## resY.mad.nexp          FALSE     NA      NA 1.000000e+00 8.904719e-122
## resY.mean              FALSE     NA      NA 5.000000e+02  2.530000e+02
## resY.mean.log1p        FALSE     NA      NA 6.216606e+00  5.537334e+00
## resY.mean.root2        FALSE     NA      NA 2.236068e+01  1.590597e+01
## resY.min               FALSE     NA      NA 5.000000e+02  2.900000e+01
## resY.min.log1p         FALSE     NA      NA 6.216606e+00  3.401197e+00
## resY.min.root2         FALSE     NA      NA 2.236068e+01  5.385165e+00
##                  max.outdoor.fctr.N max.outdoor.fctr.Y min.outdoor.fctr.N
## nImgs                  2.974000e+03       1.954000e+03       2.000000e+00
## nImgs.log1p            7.997999e+00       7.578145e+00       1.098612e+00
## nImgs.root2            5.453439e+01       4.420407e+01       1.414214e+00
## resX.mean              5.000000e+02       5.000000e+02       2.837692e+02
## resX.mean.log1p        6.216606e+00       6.216606e+00       5.651679e+00
## resX.mean.root2        2.236068e+01       2.236068e+01       1.684545e+01
## resX.min               5.000000e+02       5.000000e+02       6.100000e+01
## resX.min.log1p         6.216606e+00       6.216606e+00       4.127134e+00
## resX.min.root2         2.236068e+01       2.236068e+01       7.810250e+00
## resXY.mean             2.175740e+05       2.182778e+05       8.762615e+04
## resXY.mean.log1p       1.229030e+01       1.229353e+01       1.138085e+01
## resXY.mean.root2       4.664483e+02       4.672021e+02       2.960172e+02
## resXY.min              1.875000e+05       1.875000e+05       5.000000e+03
## resXY.min.log1p        1.214154e+01       1.214154e+01       8.517393e+00
## resXY.min.root2        4.330127e+02       4.330127e+02       7.071068e+01
## resY.mad.nexp          1.000000e+00       1.000000e+00       4.477805e-85
## resY.mean              4.861111e+02       5.000000e+02       2.530000e+02
## resY.mean.log1p        6.188492e+00       6.216606e+00       5.537334e+00
## resY.mean.root2        2.204793e+01       2.236068e+01       1.590597e+01
## resY.min               3.750000e+02       5.000000e+02       2.900000e+01
## resY.min.log1p         5.929589e+00       6.216606e+00       3.401197e+00
## resY.min.root2         1.936492e+01       2.236068e+01       5.385165e+00
##                  min.outdoor.fctr.Y max.outdoor.fctr.All.X..rcv.glmnet.Y
## nImgs                  2.000000e+00                         1.750000e+03
## nImgs.log1p            1.098612e+00                         7.467942e+00
## nImgs.root2            1.414214e+00                         4.183300e+01
## resX.mean              3.475000e+02                         5.000000e+02
## resX.mean.log1p        5.853638e+00                         6.216606e+00
## resX.mean.root2        1.864135e+01                         2.236068e+01
## resX.min               6.800000e+01                         5.000000e+02
## resX.min.log1p         4.234107e+00                         6.216606e+00
## resX.min.root2         8.246211e+00                         2.236068e+01
## resXY.mean             1.137250e+05                         2.182778e+05
## resXY.mean.log1p       1.164155e+01                         1.229353e+01
## resXY.mean.root2       3.372314e+02                         4.672021e+02
## resXY.min              3.675000e+03                         1.875000e+05
## resXY.min.log1p        8.209580e+00                         1.214154e+01
## resXY.min.root2        6.062178e+01                         4.330127e+02
## resY.mad.nexp          3.268701e-81                         1.000000e+00
## resY.mean              2.915000e+02                         5.000000e+02
## resY.mean.log1p        5.678465e+00                         6.216606e+00
## resY.mean.root2        1.707337e+01                         2.236068e+01
## resY.min               4.400000e+01                         5.000000e+02
## resY.min.log1p         3.806662e+00                         6.216606e+00
## resY.min.root2         6.633250e+00                         2.236068e+01
##                  min.outdoor.fctr.All.X..rcv.glmnet.Y
## nImgs                                    2.000000e+00
## nImgs.log1p                              1.098612e+00
## nImgs.root2                              1.414214e+00
## resX.mean                                2.837692e+02
## resX.mean.log1p                          5.651679e+00
## resX.mean.root2                          1.684545e+01
## resX.min                                 6.800000e+01
## resX.min.log1p                           4.234107e+00
## resX.min.root2                           8.246211e+00
## resXY.mean                               8.762615e+04
## resXY.mean.log1p                         1.138085e+01
## resXY.mean.root2                         2.960172e+02
## resXY.min                                3.675000e+03
## resXY.min.log1p                          8.209580e+00
## resXY.min.root2                          6.062178e+01
## resY.mad.nexp                            4.477805e-85
## resY.mean                                2.530000e+02
## resY.mean.log1p                          5.537334e+00
## resY.mean.root2                          1.590597e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.root2                           5.385165e+00
##                  max.outdoor.fctr.Final..rcv.glmnet.Y
## nImgs                                    2.825000e+03
## nImgs.log1p                              7.946618e+00
## nImgs.root2                              5.315073e+01
## resX.mean                                5.000000e+02
## resX.mean.log1p                          6.216606e+00
## resX.mean.root2                          2.236068e+01
## resX.min                                 5.000000e+02
## resX.min.log1p                           6.216606e+00
## resX.min.root2                           2.236068e+01
## resXY.mean                               2.500000e+05
## resXY.mean.log1p                         1.242922e+01
## resXY.mean.root2                         5.000000e+02
## resXY.min                                2.500000e+05
## resXY.min.log1p                          1.242922e+01
## resXY.min.root2                          5.000000e+02
## resY.mad.nexp                            1.000000e+00
## resY.mean                                5.000000e+02
## resY.mean.log1p                          6.216606e+00
## resY.mean.root2                          2.236068e+01
## resY.min                                 5.000000e+02
## resY.min.log1p                           6.216606e+00
## resY.min.root2                           2.236068e+01
##                  min.outdoor.fctr.Final..rcv.glmnet.Y
## nImgs                                    1.000000e+00
## nImgs.log1p                              6.931472e-01
## nImgs.root2                              1.000000e+00
## resX.mean                                3.045000e+02
## resX.mean.log1p                          5.721950e+00
## resX.mean.root2                          1.744993e+01
## resX.min                                 5.300000e+01
## resX.min.log1p                           3.988984e+00
## resX.min.root2                           7.280110e+00
## resXY.mean                               1.058460e+05
## resXY.mean.log1p                         1.156975e+01
## resXY.mean.root2                         3.253398e+02
## resXY.min                                1.802000e+03
## resXY.min.log1p                          7.497207e+00
## resXY.min.root2                          4.244997e+01
## resY.mad.nexp                           8.904719e-122
## resY.mean                                2.670000e+02
## resY.mean.log1p                          5.590987e+00
## resY.mean.root2                          1.634013e+01
## resY.min                                 2.900000e+01
## resY.min.log1p                           3.401197e+00
## resY.min.root2                           5.385165e+00
## [1] "newobs outdoor.fctr.Final..rcv.glmnet Y: max > max of Train range: 10000"
##      business_id outdoor.fctr.Final..rcv.glmnet .pos nImgs nImgs.log1p
## 2001       003sg                              Y 2001   167    5.123964
## 2002       00er5                              Y 2002   210    5.351858
## 2003       00kad                              Y 2003    83    4.430817
## 2004       00mc6                              Y 2004    15    2.772589
## 2005       00q7x                              Y 2005    24    3.218876
## 2006       00v0t                              Y 2006    24    3.218876
##        nImgs.nexp nImgs.root2 resX.mean.nexp resX.min.nexp resXY.mad
## 2001 2.970445e-73   12.922848  3.225211e-191 1.294998e-100         0
## 2002 6.282881e-92   14.491377  2.758174e-188 9.188626e-123         0
## 2003 8.985826e-37    9.110434  5.050852e-187 9.188626e-123         0
## 2004 3.059023e-07    3.872983  6.231837e-192 9.188626e-123         0
## 2005 3.775135e-11    4.898979  2.589612e-194 7.149792e-142         0
## 2006 3.775135e-11    4.898979  3.224442e-195 1.018963e-162         0
##      resXY.mad.log1p resXY.mad.root2 resXY.mean resXY.mean.log1p
## 2001               0               0   185382.6         12.13018
## 2002               0               0   181563.1         12.10936
## 2003               0               0   182564.8         12.11487
## 2004               0               0   191000.0         12.16003
## 2005               0               0   190645.8         12.15818
## 2006               0               0   188770.8         12.14829
##      resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2 resY.mad
## 2001         430.5608     92050        11.43010        303.3974   0.0000
## 2002         426.1022     65880        11.09561        256.6710   0.0000
## 2003         427.2760     93375        11.44439        305.5732   0.0000
## 2004         437.0355    140500        11.85297        374.8333   0.0000
## 2005         436.6301    162500        11.99844        403.1129  92.6625
## 2006         434.4777    140500        11.85297        374.8333  92.6625
##      resY.mad.log1p resY.mad.root2 resY.mean.nexp resY.min.nexp
## 2001       0.000000       0.000000  4.335495e-188  5.583037e-85
## 2002       0.000000       0.000000  8.526171e-188  3.342796e-80
## 2003       0.000000       0.000000  8.016718e-190 7.255611e-109
## 2004       0.000000       0.000000  1.437642e-192 2.398488e-145
## 2005       4.539698       9.626136  7.025181e-190 2.398488e-145
## 2006       4.539698       9.626136  2.399065e-187 9.188626e-123
##       business_id outdoor.fctr.Final..rcv.glmnet  .pos nImgs nImgs.log1p
## 3111        4051c                              Y  3111    61    4.127134
## 5473        cb1v8                              Y  5473    34    3.555348
## 6175        ewq0g                              Y  6175    58    4.077537
## 8809        oftao                              Y  8809    32    3.496508
## 9248        pzr3p                              Y  9248   298    5.700444
## 11902       zn3oa                              Y 11902    56    4.043051
##          nImgs.nexp nImgs.root2 resX.mean.nexp resX.min.nexp resXY.mad
## 3111   3.221340e-27    7.810250  1.615271e-195 9.188626e-123       0.0
## 5473   1.713908e-15    5.830952  7.900500e-192  3.481107e-57     741.3
## 6175   6.470235e-26    7.615773  5.356061e-197 9.188626e-123       0.0
## 8809   1.266417e-14    5.656854  2.694379e-190 9.188626e-123       0.0
## 9248  3.804034e-130   17.262677  9.404655e-193 3.706956e-120       0.0
## 11902  4.780893e-25    7.483315  5.059170e-203 3.082441e-126       0.0
##       resXY.mad.log1p resXY.mad.root2 resXY.mean resXY.mean.log1p
## 3111         0.000000         0.00000   186573.8         12.13659
## 5473         6.609753        27.22683   186521.8         12.13631
## 6175         0.000000         0.00000   186617.2         12.13682
## 8809         0.000000         0.00000   182203.1         12.11288
## 9248         0.000000         0.00000   183746.0         12.12131
## 11902        0.000000         0.00000   190526.8         12.15755
##       resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2 resY.mad
## 3111          431.9419    140500        11.85297        374.8333 139.3644
## 5473          431.8817     12740         9.45258        112.8716  11.1195
## 6175          431.9922     76800        11.24897        277.1281 114.1602
## 8809          426.8526    140500        11.85297        374.8333  92.6625
## 9248          428.6560     43500        10.68054        208.5665 139.3644
## 11902         436.4937    144500        11.88104        380.1316   0.0000
##       resY.mad.log1p resY.mad.root2 resY.mean.nexp resY.min.nexp
## 3111        4.944242      11.805270  3.877891e-185 9.188626e-123
## 5473        2.494816       3.334591  5.548548e-185  2.748785e-43
## 6175        4.746324      10.684578  5.383921e-183 5.879283e-105
## 8809        4.539698       9.626136  1.454377e-186 9.188626e-123
## 9248        4.944242      11.805270  2.615522e-185  1.645811e-38
## 11902       0.000000       0.000000  4.562642e-181 3.082441e-126
##       business_id outdoor.fctr.Final..rcv.glmnet  .pos nImgs nImgs.log1p
## 11995       zyrif                              Y 11995    89    4.499810
## 11996       zyvg6                              Y 11996    16    2.833213
## 11997       zyvjj                              Y 11997    27    3.332205
## 11998       zz8g4                              Y 11998   118    4.779123
## 11999       zzxkg                              Y 11999   154    5.043425
## 12000       zzxwm                              Y 12000    13    2.639057
##         nImgs.nexp nImgs.root2 resX.mean.nexp resX.min.nexp resXY.mad
## 11995 2.227364e-39    9.433981  2.552892e-180 1.076828e-106    1482.6
## 11996 1.125352e-07    4.000000  4.256714e-188 2.497728e-122       0.0
## 11997 1.879529e-12    5.196152  1.115844e-200 9.188626e-123    1482.6
## 11998 5.665668e-52   10.862780  5.049362e-193 9.188626e-123       0.0
## 11999 1.314165e-67   12.409674  1.561603e-190 9.188626e-123       0.0
## 12000 2.260329e-06    3.605551  8.694857e-179  2.107672e-95    1482.6
##       resXY.mad.log1p resXY.mad.root2 resXY.mean resXY.mean.log1p
## 11995        7.302227        38.50455   175542.2         12.07564
## 11996        0.000000         0.00000   188375.0         12.14620
## 11997        7.302227        38.50455   179504.8         12.09796
## 11998        0.000000         0.00000   183586.2         12.12045
## 11999        0.000000         0.00000   182534.7         12.11470
## 12000        7.302227        38.50455   161115.2         11.98988
##       resXY.mean.root2 resXY.min resXY.min.log1p resXY.min.root2 resY.mad
## 11995         418.9776     99552        11.50845        315.5186   0.0000
## 11996         434.0219    140000        11.84940        374.1657   0.0000
## 11997         423.6801    126630        11.74903        355.8511   2.9652
## 11998         428.4697    116000        11.66135        340.5877 169.0164
## 11999         427.2408     76800        11.24897        277.1281 106.7472
## 12000         401.3916     45998        10.73637        214.4714 185.3250
##       resY.mad.log1p resY.mad.root2 resY.mean.nexp resY.min.nexp
## 11995       0.000000       0.000000  1.321107e-189 9.188626e-123
## 11996       0.000000       0.000000  4.010869e-194 1.379016e-163
## 11997       1.377556       1.721976  2.968218e-173 2.497728e-122
## 11998       5.135895      13.000631  4.895966e-185 1.752589e-101
## 11999       4.679788      10.331854  5.383963e-186 5.879283e-105
## 12000       5.227492      13.613413  2.605171e-174  2.311343e-92
##                                id        cor.y exclude.as.feat   cor.y.abs
## .pos                         .pos  0.027497300           FALSE 0.027497300
## nImgs                       nImgs -0.014963676           FALSE 0.014963676
## nImgs.log1p           nImgs.log1p  0.047250893           FALSE 0.047250893
## nImgs.nexp             nImgs.nexp -0.003435316           FALSE 0.003435316
## nImgs.root2           nImgs.root2  0.014028124           FALSE 0.014028124
## resX.mean.nexp     resX.mean.nexp -0.022433472           FALSE 0.022433472
## resX.min.nexp       resX.min.nexp -0.022391602           FALSE 0.022391602
## resXY.mad               resXY.mad -0.011946049           FALSE 0.011946049
## resXY.mad.log1p   resXY.mad.log1p -0.014055066           FALSE 0.014055066
## resXY.mad.root2   resXY.mad.root2 -0.011364822           FALSE 0.011364822
## resXY.mean             resXY.mean -0.009002880           FALSE 0.009002880
## resXY.mean.log1p resXY.mean.log1p -0.004867571           FALSE 0.004867571
## resXY.mean.root2 resXY.mean.root2 -0.007039955           FALSE 0.007039955
## resXY.min               resXY.min -0.049458217           FALSE 0.049458217
## resXY.min.log1p   resXY.min.log1p -0.033756424           FALSE 0.033756424
## resXY.min.root2   resXY.min.root2 -0.041449898           FALSE 0.041449898
## resY.mad                 resY.mad  0.007630633           FALSE 0.007630633
## resY.mad.log1p     resY.mad.log1p -0.001526058           FALSE 0.001526058
## resY.mad.root2     resY.mad.root2  0.002557583           FALSE 0.002557583
## resY.mean.nexp     resY.mean.nexp -0.022433472           FALSE 0.022433472
## resY.min.nexp       resY.min.nexp -0.022433600           FALSE 0.022433600
##                      cor.high.X freqRatio percentUnique zeroVar   nzv
## .pos                       <NA>  1.000000        100.00   FALSE FALSE
## nImgs                      <NA>  1.033333         19.10   FALSE FALSE
## nImgs.log1p      nImgs.cut.fctr  1.033333         19.10   FALSE FALSE
## nImgs.nexp                 <NA>  1.193548         17.35   FALSE FALSE
## nImgs.root2         nImgs.log1p  1.033333         19.10   FALSE FALSE
## resX.mean.nexp             <NA>  2.000000         97.75   FALSE FALSE
## resX.min.nexp              <NA>  6.000000         11.45   FALSE FALSE
## resXY.mad                  <NA>  9.568047          4.35   FALSE FALSE
## resXY.mad.log1p  resXY.mad.nexp  9.568047          4.35   FALSE FALSE
## resXY.mad.root2       resXY.mad  9.568047          4.35   FALSE FALSE
## resXY.mean                 <NA>  6.000000         98.55   FALSE FALSE
## resXY.mean.log1p           <NA>  4.000000         90.80   FALSE FALSE
## resXY.mean.root2           <NA>  6.000000         98.20   FALSE FALSE
## resXY.min              resY.min  9.745455         37.65   FALSE FALSE
## resXY.min.log1p       resXY.min  9.745455         37.65   FALSE FALSE
## resXY.min.root2       resXY.min  9.745455         37.65   FALSE FALSE
## resY.mad                   <NA>  5.354497          9.05   FALSE FALSE
## resY.mad.log1p             <NA>  5.354497          9.05   FALSE FALSE
## resY.mad.root2             <NA>  5.354497          9.05   FALSE FALSE
## resY.mean.nexp   resX.mean.nexp  1.666667         98.15   FALSE FALSE
## resY.min.nexp              <NA>  9.824561         13.85   FALSE FALSE
##                  is.cor.y.abs.low interaction.feat shapiro.test.p.value
## .pos                        FALSE               NA         2.145811e-24
## nImgs                       FALSE               NA         1.364097e-61
## nImgs.log1p                 FALSE               NA         1.234907e-13
## nImgs.nexp                   TRUE               NA         1.763177e-72
## nImgs.root2                 FALSE               NA         4.118632e-46
## resX.mean.nexp              FALSE               NA         1.194234e-72
## resX.min.nexp               FALSE               NA         1.195403e-72
## resXY.mad                   FALSE               NA         9.894151e-67
## resXY.mad.log1p             FALSE               NA         3.868763e-59
## resXY.mad.root2             FALSE               NA         1.509232e-62
## resXY.mean                  FALSE               NA         2.964553e-36
## resXY.mean.log1p             TRUE               NA         6.980019e-43
## resXY.mean.root2             TRUE               NA         1.780045e-39
## resXY.min                   FALSE               NA         2.084930e-32
## resXY.min.log1p             FALSE               NA         1.076069e-42
## resXY.min.root2             FALSE               NA         1.752753e-35
## resY.mad                     TRUE               NA         3.711302e-48
## resY.mad.log1p               TRUE               NA         3.133148e-49
## resY.mad.root2               TRUE               NA         1.717662e-47
## resY.mean.nexp              FALSE               NA         1.194234e-72
## resY.min.nexp               FALSE               NA         1.194238e-72
##                  rsp_var_raw id_var rsp_var           max           min
## .pos                   FALSE     NA      NA  1.200000e+04  1.000000e+00
## nImgs                  FALSE     NA      NA  2.974000e+03  1.000000e+00
## nImgs.log1p            FALSE     NA      NA  7.997999e+00  6.931472e-01
## nImgs.nexp             FALSE     NA      NA  3.678794e-01  0.000000e+00
## nImgs.root2            FALSE     NA      NA  5.453439e+01  1.000000e+00
## resX.mean.nexp         FALSE     NA      NA 5.762208e-124 7.124576e-218
## resX.min.nexp          FALSE     NA      NA  9.602680e-24 7.124576e-218
## resXY.mad              FALSE     NA      NA  1.237971e+05  0.000000e+00
## resXY.mad.log1p        FALSE     NA      NA  1.172641e+01  0.000000e+00
## resXY.mad.root2        FALSE     NA      NA  3.518481e+02  0.000000e+00
## resXY.mean             FALSE     NA      NA  2.500000e+05  8.762615e+04
## resXY.mean.log1p       FALSE     NA      NA  1.242922e+01  1.138085e+01
## resXY.mean.root2       FALSE     NA      NA  5.000000e+02  2.960172e+02
## resXY.min              FALSE     NA      NA  2.500000e+05  1.802000e+03
## resXY.min.log1p        FALSE     NA      NA  1.242922e+01  7.497207e+00
## resXY.min.root2        FALSE     NA      NA  5.000000e+02  4.244997e+01
## resY.mad               FALSE     NA      NA  2.787288e+02  0.000000e+00
## resY.mad.log1p         FALSE     NA      NA  5.633821e+00  0.000000e+00
## resY.mad.root2         FALSE     NA      NA  1.669517e+01  0.000000e+00
## resY.mean.nexp         FALSE     NA      NA 1.328912e-110 7.124576e-218
## resY.min.nexp          FALSE     NA      NA  2.543666e-13 7.124576e-218
##                  max.outdoor.fctr.N max.outdoor.fctr.Y min.outdoor.fctr.N
## .pos                   2.000000e+03       1.997000e+03       2.000000e+00
## nImgs                  2.974000e+03       1.954000e+03       2.000000e+00
## nImgs.log1p            7.997999e+00       7.578145e+00       1.098612e+00
## nImgs.nexp             1.353353e-01       1.353353e-01       0.000000e+00
## nImgs.root2            5.453439e+01       4.420407e+01       1.414214e+00
## resX.mean.nexp        5.762208e-124      1.209672e-151      7.124576e-218
## resX.min.nexp          3.221340e-27       2.937482e-30      7.124576e-218
## resXY.mad              1.237971e+05       1.078962e+05       0.000000e+00
## resXY.mad.log1p        1.172641e+01       1.158893e+01       0.000000e+00
## resXY.mad.root2        3.518481e+02       3.284756e+02       0.000000e+00
## resXY.mean             2.175740e+05       2.182778e+05       8.762615e+04
## resXY.mean.log1p       1.229030e+01       1.229353e+01       1.138085e+01
## resXY.mean.root2       4.664483e+02       4.672021e+02       2.960172e+02
## resXY.min              1.875000e+05       1.875000e+05       5.000000e+03
## resXY.min.log1p        1.214154e+01       1.214154e+01       8.517393e+00
## resXY.min.root2        4.330127e+02       4.330127e+02       7.071068e+01
## resY.mad               1.942206e+02       1.853250e+02       0.000000e+00
## resY.mad.log1p         5.274130e+00       5.227492e+00       0.000000e+00
## resY.mad.root2         1.393631e+01       1.361341e+01       0.000000e+00
## resY.mean.nexp        1.328912e-110      2.530221e-127      7.667025e-212
## resY.min.nexp          2.543666e-13       7.781132e-20      1.379016e-163
##                  min.outdoor.fctr.Y max.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                   1.000000e+00                         1.999000e+03
## nImgs                  2.000000e+00                         1.750000e+03
## nImgs.log1p            1.098612e+00                         7.467942e+00
## nImgs.nexp             0.000000e+00                         1.353353e-01
## nImgs.root2            1.414214e+00                         4.183300e+01
## resX.mean.nexp        7.124576e-218                        5.762208e-124
## resX.min.nexp         7.124576e-218                         2.937482e-30
## resXY.mad              0.000000e+00                         1.059169e+05
## resXY.mad.log1p        0.000000e+00                         1.157042e+01
## resXY.mad.root2        0.000000e+00                         3.254488e+02
## resXY.mean             1.137250e+05                         2.182778e+05
## resXY.mean.log1p       1.164155e+01                         1.229353e+01
## resXY.mean.root2       3.372314e+02                         4.672021e+02
## resXY.min              3.675000e+03                         1.875000e+05
## resXY.min.log1p        8.209580e+00                         1.214154e+01
## resXY.min.root2        6.062178e+01                         4.330127e+02
## resY.mad               0.000000e+00                         1.942206e+02
## resY.mad.log1p         0.000000e+00                         5.274130e+00
## resY.mad.root2         0.000000e+00                         1.393631e+01
## resY.mean.nexp        7.124576e-218                        1.328912e-110
## resY.min.nexp         7.124576e-218                         2.543666e-13
##                  min.outdoor.fctr.All.X..rcv.glmnet.Y
## .pos                                     1.000000e+00
## nImgs                                    2.000000e+00
## nImgs.log1p                              1.098612e+00
## nImgs.nexp                               0.000000e+00
## nImgs.root2                              1.414214e+00
## resX.mean.nexp                          7.124576e-218
## resX.min.nexp                           7.124576e-218
## resXY.mad                                0.000000e+00
## resXY.mad.log1p                          0.000000e+00
## resXY.mad.root2                          0.000000e+00
## resXY.mean                               8.762615e+04
## resXY.mean.log1p                         1.138085e+01
## resXY.mean.root2                         2.960172e+02
## resXY.min                                3.675000e+03
## resXY.min.log1p                          8.209580e+00
## resXY.min.root2                          6.062178e+01
## resY.mad                                 0.000000e+00
## resY.mad.log1p                           0.000000e+00
## resY.mad.root2                           0.000000e+00
## resY.mean.nexp                          7.124576e-218
## resY.min.nexp                           7.124576e-218
##                  max.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     1.200000e+04
## nImgs                                    2.825000e+03
## nImgs.log1p                              7.946618e+00
## nImgs.nexp                               3.678794e-01
## nImgs.root2                              5.315073e+01
## resX.mean.nexp                          5.719134e-133
## resX.min.nexp                            9.602680e-24
## resXY.mad                                1.086375e+05
## resXY.mad.log1p                          1.159578e+01
## resXY.mad.root2                          3.296021e+02
## resXY.mean                               2.500000e+05
## resXY.mean.log1p                         1.242922e+01
## resXY.mean.root2                         5.000000e+02
## resXY.min                                2.500000e+05
## resXY.min.log1p                          1.242922e+01
## resXY.min.root2                          5.000000e+02
## resY.mad                                 2.787288e+02
## resY.mad.log1p                           5.633821e+00
## resY.mad.root2                           1.669517e+01
## resY.mean.nexp                          1.105028e-116
## resY.min.nexp                            2.543666e-13
##                  min.outdoor.fctr.Final..rcv.glmnet.Y
## .pos                                     2.001000e+03
## nImgs                                    1.000000e+00
## nImgs.log1p                              6.931472e-01
## nImgs.nexp                               0.000000e+00
## nImgs.root2                              1.000000e+00
## resX.mean.nexp                          7.124576e-218
## resX.min.nexp                           7.124576e-218
## resXY.mad                                0.000000e+00
## resXY.mad.log1p                          0.000000e+00
## resXY.mad.root2                          0.000000e+00
## resXY.mean                               1.058460e+05
## resXY.mean.log1p                         1.156975e+01
## resXY.mean.root2                         3.253398e+02
## resXY.min                                1.802000e+03
## resXY.min.log1p                          7.497207e+00
## resXY.min.root2                          4.244997e+01
## resY.mad                                 0.000000e+00
## resY.mad.log1p                           0.000000e+00
## resY.mad.root2                           0.000000e+00
## resY.mean.nexp                          7.124576e-218
## resY.min.nexp                           7.124576e-218
## [1] "newobs total range outliers: 10000"
## [1] TRUE
## [1] "ObsNew output class tables:"
##     lunch.-1    dinner.-1    reserve.2    outdoor.3 expensive.-1 
##        10000        10000        10000        10000        10000 
##     liquor.5      table.6    classy.-1       kids.8 
##        10000        10000        10000        10000
## [1] 0
## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"
## [1] "glb_fin_mdl_id: Final##rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## Interact.High.cor.Y##rcv#glmnet         0.507014       0.5140745
## Max.cor.Y.rcv.1X1###glmnet              0.502004       0.5290506
## All.X##rcv#glmnet                       0.502004       0.5204922
## All.X##rcv#glm                          0.502004       0.5202713
## Low.cor.X##rcv#glmnet                   0.502004       0.5012952
## MFO###myMFO_classfr                     0.502004       0.5000000
## Max.cor.Y##rcv#rpart                    0.502004       0.4985602
## Random###myrandom_classfr               0.502004       0.4969618
## Final##rcv#glmnet                             NA              NA
##                                 max.AUCpROC.OOB max.Accuracy.fit
## Interact.High.cor.Y##rcv#glmnet       0.5040041        0.5448976
## Max.cor.Y.rcv.1X1###glmnet            0.5300104        0.5009980
## All.X##rcv#glmnet                     0.5108937        0.5299353
## All.X##rcv#glm                        0.5238678        0.5236096
## Low.cor.X##rcv#glmnet                 0.4978875        0.5265996
## MFO###myMFO_classfr                   0.5000000        0.5009980
## Max.cor.Y##rcv#rpart                  0.5039318        0.5255967
## Random###myrandom_classfr             0.5059679        0.5009980
## Final##rcv#glmnet                            NA        0.5109885
##                                 opt.prob.threshold.fit
## Interact.High.cor.Y##rcv#glmnet                    0.3
## Max.cor.Y.rcv.1X1###glmnet                         0.4
## All.X##rcv#glmnet                                  0.3
## All.X##rcv#glm                                     0.3
## Low.cor.X##rcv#glmnet                              0.3
## MFO###myMFO_classfr                                0.4
## Max.cor.Y##rcv#rpart                               0.3
## Random###myrandom_classfr                          0.4
## Final##rcv#glmnet                                  0.4
##                                 opt.prob.threshold.OOB
## Interact.High.cor.Y##rcv#glmnet                    0.2
## Max.cor.Y.rcv.1X1###glmnet                         0.4
## All.X##rcv#glmnet                                  0.0
## All.X##rcv#glm                                     0.0
## Low.cor.X##rcv#glmnet                              0.0
## MFO###myMFO_classfr                                0.4
## Max.cor.Y##rcv#rpart                               0.2
## Random###myrandom_classfr                          0.4
## Final##rcv#glmnet                                   NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   N   Y
##         N   0 497
##         Y   0 501
##             err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum
## (32,60]            134.8544        122.3399        258.8735
## (60,120]           123.8999        129.2920        258.0051
## (0,32]             109.1638        117.2849        231.6803
## (120,3e+03]        108.0508        126.8451        239.6148
##             err.abs.new.sum .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## (32,60]                  NA      0.2774451      0.2434870         0.2512
## (60,120]                 NA      0.2564870      0.2605210         0.2459
## (0,32]                   NA      0.2375250      0.2374749         0.2532
## (120,3e+03]              NA      0.2285429      0.2585170         0.2497
##             .n.Fit .n.New.Y .n.OOB .n.Trn.N .n.Trn.Y .n.Tst .n.fit .n.new
## (32,60]        278     2512    243      250      271   2512    278   2512
## (60,120]       257     2459    260      251      266   2459    257   2459
## (0,32]         238     2532    237      267      208   2532    238   2532
## (120,3e+03]    229     2497    258      229      258   2497    229   2497
##             .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## (32,60]        521        0.5034566        0.4850879               NA
## (60,120]       517        0.4972768        0.4821008               NA
## (0,32]         475        0.4948730        0.4586716               NA
## (120,3e+03]    487        0.4916477        0.4718376               NA
##             err.abs.trn.mean
## (32,60]            0.4968782
## (60,120]           0.4990428
## (0,32]             0.4877480
## (120,3e+03]        0.4920221
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##       475.969008       495.761912       988.173744               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      1002.000000 
##         .n.New.Y           .n.OOB         .n.Trn.N         .n.Trn.Y 
##     10000.000000       998.000000       997.000000      1003.000000 
##           .n.Tst           .n.fit           .n.new           .n.trn 
##     10000.000000      1002.000000     10000.000000      2000.000000 
## err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean err.abs.trn.mean 
##         1.987254         1.897698               NA         1.975691
## [1] "Features Importance for selected models:"
##                All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## resX.mean.nexp                   100                   100
## resY.mean.nexp                   100                   100
## [1] "glbObsNew prediction stats:"
## 
##     N     Y 
##     0 10000
##                   label step_major step_minor label_minor     bgn     end
## 22     predict.data.new         10          0           0 261.304 277.318
## 23 display.session.info         11          0           0 277.319      NA
##    elapsed
## 22  16.015
## 23      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 1                import.data          1          0           0  24.223
## 16                fit.models          8          0           0 132.969
## 2               inspect.data          2          0           0  79.636
## 17                fit.models          8          1           1 186.824
## 20         fit.data.training          9          0           0 229.013
## 22          predict.data.new         10          0           0 261.304
## 18                fit.models          8          2           2 213.842
## 3                 scrub.data          2          1           1 116.110
## 21         fit.data.training          9          1           1 254.865
## 19                fit.models          8          3           3 224.757
## 15           select.features          7          0           0 129.924
## 14   partition.data.training          6          0           0 128.635
## 11      extract.features.end          3          6           6 127.205
## 12       manage.missing.data          4          0           0 128.135
## 13              cluster.data          5          0           0 128.567
## 9      extract.features.text          3          4           4 127.083
## 6  extract.features.datetime          3          1           1 126.926
## 7     extract.features.image          3          2           2 126.988
## 10   extract.features.string          3          5           5 127.150
## 4             transform.data          2          2           2 126.865
## 8     extract.features.price          3          3           3 127.049
## 5           extract.features          3          0           0 126.905
##        end elapsed duration
## 1   79.635  55.412   55.412
## 16 186.823  53.854   53.854
## 2  116.109  36.474   36.473
## 17 213.841  27.017   27.017
## 20 254.864  25.851   25.851
## 22 277.318  16.015   16.014
## 18 224.756  10.914   10.914
## 3  126.864  10.754   10.754
## 21 261.304   6.439    6.439
## 19 229.013   4.256    4.256
## 15 132.968   3.045    3.044
## 14 129.923   1.289    1.288
## 11 128.134   0.929    0.929
## 12 128.566   0.431    0.431
## 13 128.635   0.068    0.068
## 9  127.149   0.066    0.066
## 6  126.987   0.061    0.061
## 7  127.048   0.060    0.060
## 10 127.204   0.054    0.054
## 4  126.905   0.040    0.040
## 8  127.083   0.034    0.034
## 5  126.926   0.021    0.021
## [1] "Total Elapsed Time: 277.318 secs"